Climate data processing and impact prediction systems

ABSTRACT

Systems and methods of assessing climate transition risk. A computing system receives a user indication of a selected climate change scenario from a remote client device. The system identifies one or more energy factors from among energy sources. The system retrieves historical financial information directed to one or more securities from remote financial data sources. The system predicts one or more future returns for the securities, by applying the historical financial data and the energy factors to at least one hierarchical linear model. The system adjusts the predicted future returns based on a first climate scenario and the selected climate scenario, to form respective first and second adjusted returns. The system generates a climate transition risk for the securities based on a spread between the first adjusted returns and the second adjusted returns. The system provides a data set representing the climate transition risk to the remote client device.

TECHNICAL FIELD

The present disclosure relates to systems and methods for climate dataprocessing and impact prediction, including, for example, financialimpact and performance prediction, and more specifically to assessingclimate change risk at a security level.

BACKGROUND

Uncertainty and unpredictability within environmental phenomenon, socialphenomenon, and governance standards makes it difficult to understandtheir emerging trends, as well as the impact that the trends may haveupon investments.

Nevertheless, there is a recent movement towards environmental, social,and governance standards (ESG) investing; investing that focuses onenvironmental, social and governance standards used by sociallyresponsible investors in screening investments. An environmental aspectexamines a company's performance regarding the environment. A socialaspect examines a company's products and services and relationships withemployees, customers and the community. A governance aspect examines acompany's leadership, internal controls and shareholder rights. Therecent movement and growth towards ESG investing is evidenced by theincrease in the subsection of ESG focused exchange traded funds (ETFs).

Despite the interest in ESG investing, much of the data used to makedecisions based on environmental, social and governance standards of acompany is unreliable, as it is self-reported, and oftennon-quantifiable. For example, hundreds of firms are dedicated toanalyzing and rating company ESG performance. However it is unclear whatmechanism the firms utilize to analyze and rate company ESG performance.Furthermore, a majority of the ESG data used by firms is binary (yes/no)answers to questions related to corporate policies. The ESG data iscompiled from self-reported information and so there is a lack ofquantifiable metrics and the ability to compare between differentcompanies. Accordingly, there exists a need for quantifiable metricsrelated to ESG investing.

Moreover, the environmental aspect of ESG investing has addeduncertainty and unpredictability due to a changing climate. The impactsof climate change are far reaching and vast. Climate change has beenassociated with a rise in global sea levels, melting ice, thermalexpansion (the warming of ocean water). Additionally, the rise in globalsea levels, increase in melting ice and thermal expansion may interactwith cyclical phenomenon such as El Niño and La Nina, thus compoundingthe volatility of local environment and global climate. Climate changecreates new uncertainties for investors as rising global temperaturesand sea levels may make weather patterns more difficult to predict.Additionally the global regulatory response to climate change addsuncertainty to the performance of investments. Moreover climate changemay also increase the risk of modeling error to account for extremeweather risks. Accordingly, there remains a need for a way to betterprocess climate data to understand climate phenomenon and the impact ofclimate phenomenon on investments.

Additionally, the changing environmental conditions contribute to stockmarket volatility as the changing environmental conditions may impact,for example, the oil prices that are central to the stock market.Accordingly there is a need for realistic valuations of both energy andnon-energy companies in view of a changing climate and local environmentand providing a quantitative evaluation of companies most likely tobenefit or suffer from climate change. There is a need for systems andmethods that are able to relate climate data to economic data andquantifiably measure the impact of climate change on economicinvestment.

SUMMARY

Embodiments disclosed herein generally relate to a system, method, andnon-transitory computer readable medium for assessing climate changerisk at a security level. A computing system receives a selection of aclimate change scenario from a user operating a remote client device.The computing system generates one or more environmental metrics for oneor more energy sources based on the climate change scenario selected bythe user. The computing system converts the one or more environmentalmetrics for the one or more energy sources into one or moreprofitability indicators. The computing system retrieves one or moresets of financial information directed to one or more securities fromone or more remote financial data sources. The computing systemcorrelates at least one energy source of the one or more energy sourcesto each security of the one or more securities, by downward deployingthe one or more profitability indicators of the one or more energysources to the one or more sets of financial information of eachsecurity. The computing system generates a projected climate change riskfor each security of the one or more securities based on the one or moreenvironmental metrics for the one or more energy sources. The computingsystem provides a data set representing the projected climate changerisk to the remote client device.

In one embodiment, the system may generate public equity indices andportfolio analysis tools. In one embodiment, the generated public equityindices may be benchmarked to one or more public indices such as the S&P500 or the Russell 1000. In one embodiment, the system may uniquelyleverage machine learning to neutralize climate change risk andintegrate energy economics and financial analysis to optimize portfolioperformance.

In one embodiment, the system may focus on impacts of climate change onequity portfolios using machine learning and artificial intelligence tomodel climate change related risks. The system may assist in determiningchanges in future energy subsector investments all the while consideringcapital expenditures, cost of infrastructure investments, and carbonemissions.

In one embodiment, the system may forecast the global usage and pricesof various energy sources including for example, oil, coal, renewables(including, for example, solar and wind), nuclear, natural gas, and thelike. The system may then analyze the effect of the global usage andprices of energy sources on the profitability of all industries andindividual companies within the industry. In one embodiment, thepredicted energy source behavior and profitability of industries andindividual companies may be used to optimize portfolios for climaterisk.

In one embodiment, the system may utilize a climate model, a globalenergy model and a dynamic system of equations. The exemplary system mayallow multiple scenario generation and testing capacity, flexibility togenerate customizable scenarios, the ability to align portfolios to lowcarbon scenarios, forecasted energy usage by type and price, adjustmentsfor technology advancements and breakthroughs, aggregate level scenariosensitivities (by subsector and region), may calculate scenariosensitivities at individual security levels, and may provide the abilityto optimize any broad-based market index.

Embodiments disclosed herein also relate to a system, method, andnon-transitory computer readable medium for assessing climate transitionrisk of one or more securities. A system includes a processor and amemory having programming instructions stored thereon, which, whenexecuted by the processor, causes the processor to perform an operation.The operation includes receiving, via at least one network, a userindication of a selected climate change scenario from a remote clientdevice; identifying one or more energy factors from among one or moreenergy sources; and retrieving, over the at least one network,historical financial information directed to one or more securities fromone or more remote financial data sources. The operation furtherincludes predicting one or more future returns for the one or moresecurities, by applying the historical financial data and the one ormore energy factors to at least one hierarchical linear model (HLM). Theoperation further includes adjusting the predicted one or more futurereturns based on a first climate scenario and the selected climatescenario, to form respective first adjusted returns and second adjustedreturns. The operation further includes generating a climate transitionrisk for the one or more securities based on a spread between the firstadjusted returns and the second adjusted returns. The operation furtherincludes providing a data set representing the climate transition riskto the remote client device.

BRIEF DESCRIPTION OF THE DRAWINGS

Many aspects of the present embodiments may be better understood withreference to the following drawings. The components in the drawings arenot necessarily to scale, emphasis instead being placed uponillustrating example embodiments:

FIG. 1 is a functional block diagram illustrating a computingenvironment for climate data processing and impact predication,according to one exemplary embodiment.

FIG. 2 is a flow diagram illustrating a method of climate dataprocessing and impact prediction, according to one exemplary embodiment.

FIG. 3 is a flow diagram illustrating a method of one or more operationsincluded in a first phase of the method discussed in FIG. 2 , accordingto one exemplary embodiment.

FIG. 4 is a flow diagram illustrating a method of one or more operationsincluded in a second phase of the method discussed in FIG. 2 , accordingto one exemplary embodiment.

FIG. 5 is a flow diagram illustrating a method of one or more operationsincluded in a third phase of the method discussed in FIG. 2 , accordingto one exemplary embodiment.

FIG. 6 is a flow diagram illustrating a method of one or more operationsincluded in a fourth phase of the method discussed in FIG. 2 , accordingto one exemplary embodiment.

FIG. 7 is a functional block diagram illustrating a computingenvironment, according to one exemplary embodiment.

FIG. 8 is a screen shot illustrating an example graphical userinterface, according to one exemplary embodiment.

FIG. 9 is a screen shot illustrating an example graphical userinterface, according to one exemplary embodiment.

FIG. 10 is a screen shot illustrating an example graphical userinterface, according to one exemplary embodiment.

FIGS. 11A and 11B are screen shots illustrating an example graphicaluser interface, according to one exemplary embodiment.

FIG. 12 is a screen shot illustrating an example graphical userinterface, according to one exemplary embodiment.

FIG. 13 is a screen shot illustrating an example graphical userinterface, according to one exemplary embodiment.

FIG. 14 is a screen shot illustrating an example graphical userinterface, according to one exemplary embodiment.

FIG. 15 is a functional block diagram illustrating a computingenvironment for climate data processing and impact predication,according to one exemplary embodiment.

FIG. 16 is a functional block diagram illustrating an example transitionrisk generator of a climate data processing and impact prediction systemshown in FIG. 15 , according to one exemplary embodiment.

FIG. 17 is flow diagram illustrating a method of transition riskpredication, according to one exemplary embodiment.

FIG. 18 is a flow diagram illustrating a method of generatinguser-customizable transition risk prediction information via aninteractive graphical user interface, according to one exemplaryembodiment.

FIGS. 19A and 19B are example graphs of cumulative returns before andafter carbon emission is adjusted, respectively, according to oneexemplary embodiment.

FIGS. 20A, 20B, 20C and 20D are example graphs of cumulative returns asa function of date for various rebalancing operations, according to oneexample embodiment.

FIG. 21 is an example graph of regression coefficients of a hierarchicallinear model for two entities with respect to a supplier cost of gas anda price of oil, according to an exemplary embodiment.

DETAILED DESCRIPTION

As discussed above, the impacts of climate change may alter the behaviorof energy sources and impact the profitability of industries andindividual companies. Thus, in order to allocate investments withininvestment portfolios that mitigate for the short-term and long-termimpacts of climate change, it is beneficial to quantitatively understandthe impact of temperature changes, greenhouse gas emissions, and carbonemissions on investments.

One or more techniques disclosed herein generally relate to a system andmethod of climate data processing and impact prediction, including, forexample, financial impact and performance prediction. More specifically,the one or more techniques disclosed herein use climate data processingand impact prediction to assess climate change risk at a security level.Conventional approaches to climate data processing and impact predictingrely on a “bottom-up” approach. In other words, conventional approachesbegin at a company (or security) level, and rely on self-reportedinformation to perform data processing. These conventional techniquestypically result in a lack of quantifiable metrics and an inability tocompare climate data processing and impact prediction across two or morecompanies.

The systems and techniques of the present disclosure eliminates theconventional “bottom-up” approach. Instead, systems/techniques of thepresent disclosure implement a “top-down” approach. The “top-down”approach includes the downward correlation between one or more energysources and one or more companies. In particular, the one or moretechniques discussed herein begin at an energy source level, by treatingeach energy source as its own entity. Climate data may then be generatedfor each energy source based on one or more scenarios selected by an enduser. The claimed system then converts the generated climate data foreach energy source into one or more profitability indicators. Theclimate data to financial data conversion allows the system to downwardcorrelate each energy source to a respective industry subsector, byusing historical price returns of each industry subsector. Within eachindustry subsector, the present system can identify one or morecompanies to which each energy source maps. From this information, thepresent system is able to predict future returns for each company anduse the predicted future returns to assess climate change risk for thecompany. In other words, the downward correlation or downward deploymentmay be thought of as a translating of profitability and risk signalsassociated with each energy source (a first level) to one or moresecurities and portfolios (a second level).

In some embodiments, climate data may be generated for one or morepolicies and/or scenarios specified by a user. For example, climate datamay be generated for each specific policy, socio-economic scenario,and/or macro-economic scenario specified by the user. For eachpolicy/scenario, an energy mix may be determined. From the energy mix,the system may generate one or more metrics, such as, but not limitedto, energy supply, energy demand, energy prices, energy costs, and thelike. The one or more metrics may be used for further interpolation ofthe financial data/profitability indicators at an energy source level.

Accordingly, the one or more techniques discussed herein eliminates anydependency on companies to self-report or provide ESG information.

Additionally, such evaluation of climate risk assessment based on globalclimate inputs performed using the one or more techniques disclosedherein improves upon conventional systems, by providing a climateforecast based on profitability indicators under one or more scenariosselectable by an end user. Such assessment may provide investors withvaluable information, such as the climate risk associated with suchinvestment, under various scenarios. Such an evaluation of climate riskassessment, contrary to conventional systems and techniques, ispredictive (i.e., forward-looking), is able to provide end users (e.g.,investors) risk information that a security (or an investment) may besusceptible to climate change over time, and according to adetermination that is based on exposure to one or more energy sources(as opposed to at a company level-via the bottom up approach).

FIG. 1 is a functional block diagram illustrating a computingenvironment 100, according to one exemplary embodiment. As illustrated,computing environment 100 includes at least one client device 102, anorganization computing system 104, one or more financial data sources106 ₁, 106 ₂, and 106 _(n) (generally “data sources 106”), and one ormore environmental data sources 108 ₁, 108 ₂, and 108 _(n) communicatingvia network 105.

Network 105 may be of any suitable type, including individualconnections via the Internet such as cellular or Wi-Fi networks. In someembodiments, network 105 may connect terminals, services, and mobiledevices using direct connections, such as radio frequency identification(RFID), near-field communication (NFC), Bluetooth™, low-energyBluetooth™ (BLE), Wi-Fi™, ZigBee™, ambient backscatter communication(ABC) protocols, universal serial bus (USB), wide area network (WAN), orlocal area network (LAN). Because the information transmitted may bepersonal or confidential, security concerns may dictate one or more ofthese types of connections be encrypted or otherwise secured. In someembodiments, however, the information being transmitted may be lesspersonal, and therefore, the network connections may be selected forconvenience over security.

Network 105 may include any type of computer networking arrangement usedto exchange data. For example, network 105 may include the Internet, aprivate data network, virtual private network using a public networkand/or other suitable connection(s) that enables components in computingenvironment 100 to send and receive information between the componentsof environment 100. Although one network 105 is illustrated in FIG. 1 ,it is understood that network 105 may include one or more interconnectednetworks.

Client device 102 may be operated by a user (or customer). For example,client device 102 may be representative of a mobile device, a tablet, adesktop computer, workstation or any computing device or computingsystem having the capabilities described herein. Client device 102 mayinclude at least application 110. Application 110 may be representative,for example, of a web browser that allows access to a website or astand-alone application. Client device 102 may access application 110 toaccess one or more functionalities of organization computing system 104.

Client device 102 may communicate over network 105 to request a webpage,for example, from web client application server 112 of organizationcomputing system 104. For example, client device 102 may be configuredto execute application 110 to access content managed by web clientapplication server 112. The content that is displayed to client device102 may be transmitted from web client application server 112 to clientdevice 102, and subsequently processed by application 110 for displaythrough a graphical user interface (GUI) generated (or rendered) byclient device 102.

Organization computing system 104 may include at least web clientapplication server 112 and climate data analytics module 114. Climatedata analytics module 114 may be configured to predict the performanceof one or more companies based on a predicted environmental state.Climate data analytics module 114 may include climate data integrator116, a financial data integrator 118, optimization module 120, outputmodule 122, environmental-to-financial (EF) converter 124, and climatemodule 130.

Each of climate data integrator 116, a financial data integrator 118, anoptimization module 120, an optimization module 122, anenvironmental-to-financial (EF) converter 124, and climate module 130may be comprised of one or more software modules. The one or moresoftware modules may be collections of code or instructions stored on amedia (e.g., memory of organization computing system 104) that representa series of machine instructions (e.g., program code) that implementsone or more algorithmic steps. Such machine instructions may be theactual computer code a processor of organization computing system 104interprets to implement the instructions or, alternatively, may be ahigher level of coding of the instructions that is interpreted to obtainthe actual computer code. The one or more software modules may alsoinclude one or more hardware components. One or more aspects of anexample algorithm may be performed by the hardware components (e.g.,circuitry) itself, rather as a result of an instructions.

Climate data integrator 116 may be configured to receive climate relateddata from one or more environmental data sources 108. As illustrated,organization computing system 104 may be in communication with one ormore environmental data sources 108.

One or more environmental data sources 108 may be representative of oneor more computer systems hosting environmental data. Such environmentaldata sources 108 may include, but are not limited to governmental andinternational organizations and their databases such as the UnitedStates Energy Information Administration (US EIA), the InternationalMonetary Fund's World Economic Outlook (IMF WEO), the US Department ofEnergy (DOE), the United Nations (UN), the International Energy Agency(IEA), and the National Air and Space Administration's Goddard Institutefor Space Studies (NASA GISS).

In some embodiments, one or more environmental data sources 108 mayfurther include academic and research institutes such as theMassachusetts Institute of Technology (MIT). Academic and researchinstitutes may provide climate or financial related databases and modelssuch as MIT's Emissions Prediction and Policy Analysis model (MIT EPPA).Climate data integrator 116 may retrieve from one or more environmentaldata sources 108 climate or environmental data such as atmosphericcarbon dioxide levels, global surface temperatures, gross domesticproduct (GDP), population, energy intensity of GDP, carbon intensity ofenergy, carbon dioxide emissions from energy, measures of the effect oftechnological progress on energy, measures of the effect of energy priceon energy intensity, cost by electrical source, electrical generation byenergy source, and fraction of revenue put into process research anddevelopment by energy source.

In some embodiments, one or more environmental data sources 108 mayinclude sources of corporate social responsibility (CSR) ratings orother environmental, governance, political, geographic, and socialmetrics. Other data may include financial and other security level datafrom FACTSET®. In some embodiments, other data may include regulatoryreports filed to the Securities and Exchange Commission (SEC), companydisclosures through press releases, corporate sustainability reports,industry specific information such as Integrated Resource Plans filed byelectric utilities, analyst reports, self-reported climate disclosurereports and other commercial and non-profit sources of information.

In some embodiments, one or more environmental data sources 108 mayfurther includes the carbon dioxide information analysis center (CDIAC)of the National Climatic Data Center (NCDC), the National Ocean andAtmospheric Administration (NOAA) and the International Panel on ClimateChange (IPCC). In one embodiment, data related to selected features maybe extracted from the climate related data at climate data integrator116. Selected features may include one or more of the projected grossdomestic product (GDP) level, carbon tax, and the subsidy on renewables.

Climate data integrator 116 may be configured to aggregate and processthe environmental and/or climate information received from one or moreenvironmental data sources 108.

Financial data integrator 118 may configured to retrieve financial datafrom one or more financial data sources 106. As illustrated,organization computing system 104 may be in communication with one ormore financial data sources 106.

One or more financial data sources 106 may be representative of one ormore computer systems hosting environmental data. Such environmentaldata sources 106 may include, but are not limited to, stock exchanges,academic journals and the like. Data retrieved may also include economicdata such as gross domestic product (GDP), interest rates, data on taxesand subsidies, energy prices, and carbon prices.

In some embodiments, such financial data sources 106 may further includedata on the return and performance of securities such as FACTSET®. Insome embodiments, financial data integrator 118 may collect data oncompanies listed on major stock exchanges such as the New York StockExchange (NYSE), NASDAQ, the Toronto Stock Exchange (TSX), the TorontoStock Exchange-Venture (TSX-V), the London Stock Exchange (LSE), theStock Exchange of Hong Kong (SEHK), and the Australian SecuritiesExchange (ASX). Financial data integrator 118 may be configured todetermine returns for each of the constituents along with the meanreturns for all constituents.

In one embodiment, financial data sources 106 may collect data regardingthe constituents of an index. In one embodiment, the impact of climatechange on investments may be determined and recalculated on a quarterlybasis, although any suitable time period may be used. The constituentsof the index may differ for each recalculation. The financial data mayinclude data spanning a number of months or years. In one embodiment,the financial data may include historical returns for securities for atleast eight historical years. In one embodiment, securities with limiteddata (e.g., securities with less than six months of data) may beexcluded. In one embodiment, securities demonstrating un-realisticreturns may be excluded.

Climate module 130 may be configured to receive climate data fromclimate data integrator 116. In some embodiments, climate module 130 maysimulate the future price, supply, and demand for energy sources usingdata received from climate data integrator 116. Such energy sources mayinclude oil, gas, coal, biofuels, renewable fuel, renewable electric,hydro, new technology and nuclear. In some embodiments, climate module130 may take as input from, for example, client device 102, a scenarioupon which to base the analysis. Accordingly, climate module 130 isconfigured to project climate data for each energy source based on theselected scenario.

EF converter 124 may be configured to receive output from climate module130, and convert the output received from climate module 130 into one ormore profitability indicators. In other words, EF converter 124 may beconfigured to convert climate related metrics into financial relatedmetrics. By converting climate related metrics for each energy sourceinto financial related metrics, climate data analytics module 114 maygenerate one or more indicators for each energy source as if each energysource represents a company or business subsector.

EF converter 124 may use the simulations of the future price and demandfor energy sources produced by the climate module 130 to generate aprofitability estimate. In some embodiments, the profitabilityestimation is based on one or more algorithms 126 (e.g., a system ofdifference equations). The profitability simulations executed by EFconverter 124 may generate financial time series values such as netpresent value (NPV), return on investment (ROI), debt coverage ratio(DCR), and internal rate of return (IRR) for each of the one or moreenergy sources. The financial time series values may be based on anestimate of the total revenue of energy sources and the total cost ofinvestment in energy sources.

In some embodiments, the total revenue of energy sources may be basedupon energy demand and price that accounts for macro-economic indicatorssuch as GDP, population, average overall cost of supply, energy supplycapital lifetime and discount rate percentage, climate change indicatorssuch as temperature, sea level estimates, amount of greenhouse gasemissions (GHG), and water consumption, as well as policy indicatorssuch as carbon price, subsidies and GDP, population, interest rates,capital efficiency.

Optimization module 120 may generate company level predictions bydownward deployment of the profitability indicators generated for eachenergy source to a security level data. In other words, optimizationmodule 120 may correlate each energy source with one or more businesssubsectors based on the profitability indicators generated by EFconverter 124. In some embodiments, optimization module 120 may identifythe most correlated energy sources under one or more scenarios (e.g., abusiness as usual scenario, a carbon emission scenario, etc.) for eachsecurity.

In some embodiments, after optimization module 120 identifies theindustry subsectors correlated to each energy source, optimizationmodule 120 may downward deploy the information another level, bycorrelating companies within each subsector to a particular energysource. Such company level correlations may be made based on, forexample, comparative positioning of companies within the industrysubsector. Companies may be comparatively positioned within an industrysubsector based on, for example, their size, growth, and/or volatilitywithin the subsector. In one embodiment, optimization module 120 mayutilize statistical models such as, but not limited to Monte-Carlosimulations, to test the identified industry subsector's most correlatedenergy categories (discussed further below).

In some embodiments, once optimization module 120 correlates each energysource to a business subsector, optimization module 120 may beconfigured to project environmental performance of the businesssubsector, based on the related climate data of the correlated energysource generated by climate module 130.

Output module 122 may be configured to provide a scoring based, forexample, on a company's exposure to the energy industry and climatechange using environmental, social and governance (ESG) metrics and theresults from the optimization module 120. The scoring may provide aquantitative and objective means for comparing the performance ofdifferent companies in accordance with ESG standards.

In some embodiments, output module 122 may produce a website, accessibleby one or more users via application 110 executing on client device 102.The website may provide a dashboard that allows users to view resultsgenerated by climate module 130, EF converter 124, and/or optimizationmodule 120. In some embodiments, output module 122 may generate one ormore data files for electronic transfer to client device 102. EFconverter 124 may provide the output produced by output module 102 toone or more client devices 102. In some embodiments, the results fromthe optimization module 120 may be used to project portfolio performancerelative to multiple emission scenarios, optimize a portfolio based onselected scenarios, and climate and energy risk parameters associatedwith the selected scenarios.

Some portions of above description describe the embodiments in terms ofalgorithms and symbolic representations of operations on information.These algorithmic descriptions and representations are commonly used bythose skilled in the data processing arts to convey the substance oftheir work effectively to others skilled in the art. These operations,while described functionally, computationally, or logically, areunderstood to be implemented by computer programs or equivalentelectrical circuits, microcode, or the like. Furthermore, it has alsoproven convenient at times, to refer to these arrangements of operationsas modules, without loss of generality. The described operations andtheir associated modules may be embodied in specialized software,firmware, specially-configured hardware or any combinations thereof.

Those skilled in the art will appreciate that organization computingsystem 104 may be configured with more or less modules to conduct themethods described herein with reference to FIGS. 2-6 . As illustrated inFIGS. 2-6 , the methods shown may be performed by processing logic thatmay comprise hardware (e.g., circuitry, dedicated logic, programmablelogic, microcode, etc.), software (such as instructions run on aprocessing device), or a combination thereof. In one embodiment, themethods shown in FIGS. 2-6 may be performed by one or more specializedprocessing components associated with components 112-130 of organizationcomputing system 104 of FIG. 1 .

FIG. 2 is a flow diagram illustrating a method 200 of climate dataprocessing and impact prediction, according to one exemplary embodiment.As illustrated, method 200 may have four phases.

At phase 202, climate data analytics module 114 may generate one or moreenvironmental metrics for one or more energy sources based on a selectedscenario. The one or more operations implemented at phase 202 arediscussed in further detail below, in conjunction with FIG. 3 .

At phase 204, climate data analytics module 114 may generate one or moreprofitability indicators for each of the one or more energy sources. Forexample, climate data analytics module 114 may generate the one or moreprofitability indicators based on the one or more environmental metricsgenerated during phase 202. The one or more operations implemented atphase 204 are discussed in further detail below, in conjunction withFIG. 4 .

At phase 206, climate data analytics module 114 may correlate eachenergy source with a financial subsector based on the one or moreprofitability indicators. For example, climate data analytics module 114may downward deploy the profitability indicators generated in phase 204to one or more financial subsectors down to one or more companies withineach financial subsector. The one or more operations implemented duringphase 206 are discussed in further detail below, in conjunction withFIG. 5 .

At phase 208, climate data analytics module 114 may generate an outputdata set for a user. For example, climate data analytics module 114 mayprovide a scoring based on a company's exposure to the energy industryand climate change using environmental and social and governance (ESG)metrics. The one or more operations implemented during phase 208 arediscussed in further detail below, in conjunction with FIG. 6 .

FIG. 3 is a flow diagram illustrating a method 300 of operation duringphase 202, according to one exemplary embodiment. Method 300 may beginat step 302.

At step 302, organization computing system 104 may receive a request foranalysis from client device 102. In some embodiments, organizationcomputing system 104 may receive the request for climate impactprediction via a website accessed by client device 102 via application110 executing thereon.

At step 304, organization computing system 104 may prompt the user ofclient device 102 to select one or more scenarios for the climate impactprediction. Possible scenarios may include a carbon-minimum scenario, acarbon-maximum scenario, and a business as usual scenario. In oneembodiment, the scenario may be selected by an automated program. Acarbon-minimum scenario may be associated with an approximately 2.8degree temperature increase in average global temperature. Acarbon-maximum scenario may be associated with an approximately 4.6degree temperature increase in average global temperature. A business asusual scenario may be determined by the United States Department ofEnergy.

The scenario selection may specify metrics related to the growth ofpopulation, gross domestic product (GDP) per capita, greenhouse gas(GHG) emissions, and removals other than CO₂ emissions. The user mayalso specify policy changes, including a carbon price, subsidy, or taxaimed at a particular energy source, policies for more rapid improvementin end-use energy efficiency, utility performance standards, technicalbreakthroughs affecting the cost and carbon intensity of particularenergy sources, or an earlier retirement of existing energy supplysources. The climate data input into the climate model 111 may includeindicators such as gross domestic product (GDP), energy source tax andsubsidies, interest rates, and capital efficiency percentage. The energysources may include, without being limited to, coal, gas, oil,renewables, hydro, nuclear, new tech, and biofuel. Climate modelsincluding ensemble models may be used to generate the profitabilityindicators.

At step 306, organization computing system 104 may input one or moremetrics associated with the selected one or more scenarios to generateone or more environmental metrics for each energy source. For example,climate module 130 may input the one or more metrics into anenergy-climate policy simulator.

In some embodiments, climate module 130 may input the one or moremetrics into an Energy-Rapid Overview and Decision-Support (En-ROADS)simulator, available from Climate Interactive©. Climate module 130 mayleverage En-ROADS simulator to provide information relating to changesin global GDP, energy efficiency, R&D results, carbon price, fuel mix,and other factors will change carbon emissions and impacts on globalclimate, such as temperature change. En-ROADS simulator may receive, asinput from climate module 130, a measure of global GDP, energyefficiency, R&D results, carbon price, fuel mix, and the like. Suchinputs may be received by climate module 130 from climate dataintegrator 116.

In some embodiments, climate module 130 may utilize a specializedsoftware configured to execute simulations with improved model quality,data connections, and advanced algorithms. In some embodiments, En-ROADSsimulator may be utilized with Vensim programming. En-ROADS simulatorand other energy-climate policy simulations may apply the principles ofsystem dynamics modeling. In some embodiments, the En-ROADS model maysolve a system of nonlinear ordinary difference equations by Eulerintegration to generate one or more output values. In some embodiments,the En-ROADS model may be configured to solve a system of nonlinearordinary difference equations by Euler integration using a time step ofone-eighth of a year. In some embodiments, the En-ROADS simulator maystart at 1990 and simulate through 2100.

Using the En-ROADS simulator, climate module 130 may produce a solutionhorizon that is recursive-dynamic, and determine a solution that is apartial equilibrium that accounts for price elastic demand, andincorporating short and long term supply, demand, and pricing effects.Although the En-ROADS simulator may be capable of performingcomputationally intensive sensitivity analysis using, for example, MonteCarlo optimization, in some embodiments, climate module 130 may beconfigured to utilize simple scenario simulations. In some embodiments,the climate module 130 may execute En-ROADS simulator locally withinorganization computing system 104. In some embodiments, climate module130 may execute En-ROADS simulator as a part of a networked computingenvironment 100.

Climate module 130 may generate a market price of delivered fuels,demand of delivered fuels, average embodied fixed cost of fuel capacity,instant variable cost of fuel supply, carbon tax for delivered fuels,source subsidy for delivered fuels over time, fuel completing capacity,actual retirement rate, and delivered fuel capital lifetime for eachfuel type. Climate module 130 may also generate a measure of the marketprice of electricity and the production for electricity, averageembodied fixed cost of electric capacity, instant variable coast ofelectric supply, source subsidy to electric producers over time,electricity completing capacity, actual retirement rate, and electricsupply capital lifetime for each electric source.

At step 308, organization computing system 104 may receive, as output,one or more environmental metrics for each of the one or more energysources. For example, climate module 130 may generate one or moreenvironmental metrics for each of oil, gas, coal, biofuels, renewablefuel, renewable electric, hydro, new technology, and nuclear energysources.

FIG. 4 is a flow diagram illustrating a method 400 of operation duringphase 204, according to one exemplary embodiment. Method 400 may beginat step 402.

At step 402, climate data analytics module 114 may identify one or morevalues in one or more environmental metrics. For example, EF converter124 may identify one or more profitability indicators to be generated.For each profitability indicator, optimization module 120 may identifythose environmental metrics output by climate module 130 that may beused to generate each of the one or more profitability indicators. Usinga specific example, to generate net present value (NPV), EF converter124 may identify one or more metrics associated with internal cost ofthe energy source.

At step 404, climate data analytics module 114 may input the one or moremetrics output by climate module 130 into EF converter 124. For example,EF converter 124 may generate one or more profitability indicators foreach energy source under each scenario based on the one or more metricsoutput by climate module 130.

EF converter 124 may generate one or more profitability indicators, suchas absolute annualized capital cost, absolute variable cost, absoluterevenue, gross margin, and unit gross margin using algorithm 126.Additionally, for capital installed at a specified year for eachdelivered fuel type and for each source of electricity EF converter 124may generate one or more profitability indicators related to, withoutbeing limited to, the capacity of the investor, the investor grossmargin, the equivalent overnight capital costs, investment, equity,debt, investor net cash flow, net present value (NPV), internal rate ofreturn (IRR), and the return on investment (ROI). In some embodiments,EF converter 124 may generate the total NPV and a weighted average ofIRR and ROI for all of the constituents.

Accordingly, the profitability indicators generated by the EF converter124 may include a measure of the NPV, ROI, DCR, IRR and the like foreach of the one or more energy sources. In one embodiment the generatedprofitability indicators may be reflect regional variability.

At step 406, climate data analytics module 114 may receive, as output,one or more profitability indicators based on the environmental metrics.For example, climate data analytics module 114 may receive from EFconverter 124 one or more profitability indicators generated byproviding metrics generated by climate module 130 into algorithm 126.The one or more profitability indicators provide a financial outlook foreach energy source as if each energy source was a given financialsubsector or company.

FIG. 8 is a screen shot illustrating example graphical user interface(GUI) 800, according to one exemplary embodiment. As illustrated in GUI800, there may be one or more inputs 802 and one or more outputs 804.One or more inputs 802 may correspond to the one or more environmentalmetrics generated by climate module 130, discussed above in conjunctionwith FIG. 3 . One or more outputs 804 may correspond to the one or moreprofitability indicators generated by climate data analytics module 114.

FIG. 5 is a flow chart illustrating a method 500 of operations duringphase 206, according to one exemplary embodiment. Method 500 may beginat step 502.

At step 502, climate data analytics module 114 may retrieve (e.g., pull)financial information from one or more subsector indices. For example,financial data integrator 118 may retrieve from one or more financialdata sources 106 the financial data of one or more subsector indices. Insome embodiments, the one or more data sources 106 hosting the financialdata may be global industry classification standards (GICS) developed byMorgan Stanley Capital International (MSCI). In some embodiments,financial data integrator 118 may retrieve financial information for 24GICs subdivisions). Climate data analytics module 114 may furtherretrieve ROI information under one or more pre-selected scenarios (e.g.,business-as-usual).

In some embodiments, financial data integrator 118 may identify aportfolio including a list of securities. In some embodiments, the listof securities may correspond with a benchmark such as the S&P 500,Russell 100 or the like. In some embodiments, the climate data analyticsmodule 114 may be provided with the data set from a user computer. Insome embodiments, the portfolio or benchmark of interest may be selectedbased on an index requirement. The index requirement may be based on aMarket Focus (i.e., Domestic, International), Fund theme (i.e., Sectorof Interest: Technology, Energy), Factor Choices (i.e., Growth, Value)and the like. Once identified, climate data analytics module 114 mayrequest data and information related to the list of securities from oneor more financial data source 106 in communication with the climate dataanalytics module 114. Data sources that provide data and informationrelated to the list of securities may include financial data andsoftware resources that provide real-time news and quotes, company andportfolio analysis, multi-company comparisons, industry analysis,company screening, portfolio optimization and simulation, predictiverisk measurements, alphatesting and tools to value and analyze fixedincome securities and portfolios. This data and information may includefinancial data and information.

In some embodiments, the financial data integrator 118 may perform thediscussed identification and retrieval steps. Financial data integrator118 may pull time series data on stock prices for a benchmark, calculatethe returns from the time series data on stock prices for the benchmark,calculate the average returns for specific securities within thebenchmark and find the downside variance-covariance of all individualsecurities within the benchmark based on Sortino ratios. The financialdata integrator 118 may also determine one or more of a data sets'minimum and maximum weights, sector diversity and financial riskcoefficients.

At step 504, climate data analytics module 114 may smooth the retrievedfinancial information within a pre-defined window. For example,optimization module 120 may smooth the retrieved financial informationfor the one or more subsector indices with a five-point window locallyweighted regression using, for example, data from a previous range ofyears (e.g., the previous 6 years). Exemplary weighted regressions mayinclude locally weighted regressions such as, but not limited to, alocally weighted scatterplot smoothing (LOWESS). Smoothing the retrievedfinancial information aids in identifying relationships among variables,as well as trends in the information.

At step 506, climate data analytics module 114 may downward deploy theone or more profitability indicators generated by EF converter 124 toone or more financial subsectors, to identify sector level correlations.Optimization module 120 may identify correlations between the one ormore energy sources and one or more financial subsectors using, forexample, Kendall's tau coefficient. Kendall's tau coefficient is used tomeasure association between the one or more profitability indicators ofeach energy source and the financial information of the one or morefinancial subsectors. Kendall's tau coefficient may provide a ranking ofrelevance between each financial subsector and each energy source.

Such operations may include:

  def plot_smoothing ( ) :  fig = plt.figure (figsize=(20,12)) timely_sec = sec.loc [2003:2018]  for idx, ind in enumerate(timely_sec.columns [20:24] ) :   smoothed = ent.smooth(timely_sec.loc[:,ind],5)   ax = fig.add_subplot (2,2,idx+1)  ax.plot(timely_sec.loc[:,ind],′k =.′, linewidth=1)  ax.plot(smoothed,′r′,linewidth=2)  ax.set_title(timely_sec.loc[:,ind].name, size=18)  ax.set_xlabel(′Year′,size=18)   ax.set_ylabel(′Qtrly Period RetPropn′,size=18)   ax.tick_params(labelsize=18)   ax.grid(False) fig.suptitle(′Smoothed Returns′,size=30) fig.subplots_adjust(top=.9,bottom=0.08,left=0.1,right=0.9m   hspace=0.4, wspace=0.3)  return

FIG. 9 is a screen shot illustrating example GUI 900, according to oneexemplary embodiment. As illustrated in FIG. 9 , GUI 900 may include oneor more histograms. Each histogram represents a correlation between oneor more energy sources and one or more financial subsectors. The peaksof each histogram may represent the most correlated energy sources andfinancial subsectors.

Referring back to FIG. 5 , at step 508, climate data analytics module114 may identify one or more energy sources that correspond to each ofthe one or more financial subsectors. In some embodiments, optimizationmodule 120 may identify the four most relevant energy sources to each ofthe twenty-four financial subsectors. In those embodiments in which allenergy sources are equally correlated with a particular financialsubsector, optimization module 120 may select those four energy sourcesthat are the “greenest.” Greenest, for example, may correspond to theenergy source have the smallest Carbon footprint.

At step 510, climate data analytics module 114 may retrieve financialdata related to one or more securities. For example, financial dataintegrator 118 may retrieve from one or more financial data sources 106monthly return information for one or more securities.

At step 512, climate data analytics module 114 may smooth the retrievedfinancial information. For example, optimization module 120 may smooththe security level information using a weighted regression. Suchweighting regression may include LOWESS regressions. Smoothing theretrieved financial information aids in identifying relationships amongvariables, as well as trends in the information.

At step 514, climate data analytics module 114 may map the one or moresecurities to a respective financial subsector. For example,optimization module 120 may identify one or more financial subsectors towhich each respective securities applies. In some embodiments, climatedata analytics module 114 may procure economic classification data for agiven security, such as, without being limited to, economicclassification according to the Global Industry Classification Standard(GICS). Each economic sector may be mapped to a subset of an energysource. Using this mapping, climate data analytics module 114 may mapeach security to a given energy source from a financial subsector of itseconomic sector.

At step 516, climate data analytics module 114 may correlate thesecurity level information with the mappings from step 508. For example,optimization module 120 may correlate the security level informationwith the four most relevant energy sources identified above inconjunction with step 508.

At step 518, climate data analytics module 114 may identify one or moreenergy sources that correspond to each of the one or more securities.For example, continuing with the example in step 508 above, optimizationmodule 120 may identify the two most relevant energy sources for each ofthe one or more securities. In those embodiments in which three or moreenergy sources are equally relevant to a respective security,optimization module 120 may break any ties by, for example, selectingthe “greenest” energy source.

FIG. 10 is a screen shot illustrating example GUI 1000, according to oneexemplary embodiment. As illustrated, GUI 1000 may include one or moregraphical representations of returns by subsector from 2016 to 2018.Such GUI 1000 may be accessible to user via client device 102.

FIG. 6 is a flow diagram illustrating a method 600 of operations duringphase 208, according to one exemplary embodiment. Method 600 involvespredicting future climate impact for each security (or financialsubsector) and presenting that information to the requestor. In general,according to method 600, one or more mapped energy sector(s) may be usedas input variable(s) to forecast a performance of a security. Method 600may use correlations (described below) to identify energy mappings. Onceenergy mappings are identified, profitability indicators of the mappedenergy sources (e.g., a ROI) may be used as an input (e.g., explanatoryvariables) to predict scenario-specific return performance of thesecurities. Method 600 may begin at step 602.

At step 602, climate data analytics module 114 may be configured togenerate future climate predictions for each security using a regressionmodel. Such regression models may include a linear regression model. Forexample, climate data analytics module 114 may use the identified energymappings by identifying one or more profitability indicators associatedwith a given security. As input to the regression model, climate dataanalytics module 114 may provide the one or more profitabilityindicators to predict a scenario-specific return performance for thesecurity. Further, in some embodiments, for each security, output module122 may retrieve climate data generated by climate module 130 for eachmapped energy source. In some embodiments, output module 122 mayretrieve the climate data according to one or more selected scenarios.The retrieved climate data includes future climate data projections.Output module 122 may then project the future climate impact for eachsecurity using the future climate data projections and historical pricereturns by leveraging one or more linear regression algorithms. Theprojections may be compiled in a data set to be provided to the user.

At step 604, output module 122 may generate a sensitivity measure foreach security by analyzing the spread of each security's projectedreturns across each of the given scenarios.

In some embodiments, output module 122 may generate one or more scoresfor each securities' returns across one or more scenarios. For example,output module 122 may generate three scores for each of a business asusual (BAU) scenario, a carbon max scenario, and a carbon min scenario.

In some embodiments, output module 122 may generate a spread of asecurity's expected performance using expected returns. A higher spreadindicates a higher sensitivity towards economic and policy changes. Alow spread indicates lower risk towards these changes. A min max areavalue indicates the min max area value for stocks in a security'sportfolio. A min max area value may be calculated using:

${MMA_{s}} = {\frac{1}{| {\overset{¯}{R_{s}}( {BAU} )} |}{❘{{{\sum}_{t = {t*}}^{T}R_{x,{ts}}} - R_{m,{ts}}}❘}}$

where t*>TOD(Time of Day), R_(x,ts), are expected returns computingusing regression to carbon max scenario, and R_(m,ts) are expectedreturns computing using carbon min scenario.

In some embodiments, output module 122 may generate a min max draw downscore. The min max draw down score may reflect the spread of aconstituent's expected performance as computed using Expected Returnsfor the Carbon Minimum and Carbon Maximum scenarios. A higher spread maybe indicative of a higher risk towards economic and policy changes.

In some embodiments, output module 122 may generate an affinity towardscarbon minimum score (ACM). The ACM may reflect the spread of differencebetween the expected returns for the carbon minimum scenario and theexpected returns of the business as usual scenario. A positive score forthe ACM may indicate that a constituent is expected to perform better acarbon minimum scenario. A negative score for the ACM may indicate thatthe constituent is expected to perform better for a carbon maximumscenario.

The ACM may be defined as follows:

${ACM_{S}} = {\frac{1}{| {\overset{¯}{R_{s}}( {BAU} )} |}{\sum\limits_{t = {t*}}^{T}( {R_{m,{ts}},\ R_{{BAU},{ts}}} )}}$

where t*>TOD (Time of Deviation), R_(BAU,ts) are the expected returnscomputed using a regression to carbon business as usual scenario,R_(m,ts) are the expected returns computed using a carbon min scenario,and R _(S) are the mean returns computed using the business as usualscenario.

In one embodiment, output module 122 may generate an affinity towardscarbon maximum score (AXM). The AXM may reflect the spread of differencebetween the expected returns for the carbon maximum scenario and theexpected returns of the business as usual scenario. A positive AXM mayindicate that a constituent is expected to perform better under a carbonmaximum scenario while a negative score may indicate that a constituentis expected to perform better under a carbon minimum scenario.

The AXM may be defined as follows:

${AXM_{s}} = {\frac{1}{| {\overset{¯}{R_{s}}( {BAU} )} |}{\sum\limits_{t = {t*}}^{T}( {R_{X,{ts}},\ R_{{BAU},{ts}}} )}}$

where t*>TOD (Time of Deviation) R_(x,ts) are the expected returnscomputed using a regression to carbon max scenario, R_(BAU,ts) are theexpected returns computed using a business as usual scenario, and R_(S)(BAU) are the expected returns computed using the business as usualscenario.

In some embodiments, output module 120 may compute the securities'energy dependency using specialized software that is configured forcomputationally intensive calculations. In one embodiment, pythonScikit-learn may be used to run multiple support vector machine (SVM)regressions in order to compute the constituents' energy dependency.SVMs may be used for their effectiveness in high dimensional spaces,ability to be run in cases where the number of dimensions is greaterthan the number of samples. SVMs may also be memory efficient as theymay use support vectors including a subset of training points in thetraining function. Additionally SVMs may be versatile as differentkernel functions can be specified for the decision function.

At step 606, output module 122 may optimize the one or more projectedvalues for each security. For example, output module 122 may generateone or more data sets that comprise the one or more projected values foreach security. Such optimization equation may be:

{grave over (W)}R −(1−

){grave over (W)}ΣW

where

∈(0,1)|∀s∈S, w_(s)≥0.001 (Min weight constraint), w_(s)≤0.05 (Max weightconstraint), and Σw_(s)=1.0

In one embodiment, output module 122 may use securities sensitivities asa constraint and optimize the data set using a Markowitz OptimizationProcess. In one embodiment, statistical analysis and big data may beused to determine the objective function in the Markowitz Optimizationprocess. In one embodiment, a convex optimizer package with a betareduction component may be used. In general, any suitable optimizationmodel may be used to optimize the data in accordance with embodimentsdescribed herein.

In some embodiments, output module 122 may optimize the data set forhigher returns, less risk and higher carbon reductions. Output module122 may provide a weighting for constituents of the data set such thatany data set constructed from the provided weightings would have a loweraggregate climate risk in comparison to the benchmark.

In some embodiments, output module 122 may optimize the data set towardspreferred scenarios. For example, if a user were interested in investingin a portfolio that outperforms in carbon-min scenario output module 122may re-weight the securities in accordance with the user'ssocioeconomic, policy and climate preferences.

In some embodiments, output module 122 may utilize a convex optimizationutility that is based on Python. For example, output module 122 may usea Basic Linear Algebra Subprograms (BLAS) that are capable ofefficiently performing matrix based mathematical operations; therebyallowing the output module 122 to optimize larger amounts of data usingalgorithms that utilize matrices without taxing either the processor ormemory components of the server system 102. In addition or as analternative, output module 122 may solve a non-convex optimizationproblem using a Generalized Simulated Annealing for Global Optimizationpackage. In one embodiment, output module 122 may analyze theconditional value at risk to assess the likelihood that a specific losswill exceed the value at risk.

In some embodiments, output module 122 may optimize the data set andprovide a set of weights. The set of weights may be generated on varioustime scales including every week, month, quarter, or year. The weightsmay be used to compute indexes, portfolios, and holdings for exchangetraded funds.

In one embodiment, the weightings produced by output module 122 may beused to construct smart climate indices that are designed to track wellestablished global indices such as the S&P500, Russell 1000 and thelike. However, the smart climate indices may be optimized to lower beta,volatility and tracking error while increasing climate impact andfinancial performance. The smart climate indices may also be used forETF creation and benchmarked performance evaluations.

In some embodiments, the weightings produced by the output module 122may be used to construct a sensitivity restricted portfolio. Thesensitivity restricted portfolio may be configured to maximize thereturns of the portfolio subject to an ESG based constraint and anEnergy Mix Transition Risk (EMTR) score constraint.

In some embodiments, the weightings produced by the output module 122may be used to construct a carbon minimum biased portfolio by maximizingthe returns of the portfolio subject to an ESG based constraints and anACM constraint.

In some embodiments, the weightings produced by output module 122 may beused to construct a carbon minimum biased profile by maximizing thereturns of the portfolio subject to ESG constraints and an AXMconstraint.

In some embodiments, the weightings produced by the output module 122may be used to provide portfolio optimization for climate and energyvolatility, and may include scenario analysis and shock testing. Theweightings may be used to evaluate client portfolios and climate impact.

In some embodiments, output module 122 may provide users with newweights with no addition or subtraction of constituents from the dataset. In one embodiment, output module 122 may produce optimized datasets that may provide the best opportunities for gain with the leastrisk, materially reduce risk of divergence in the global economy frombaseline to high/low carbon scenarios, contain constituents havinggreater stability and improved performance under more climate friendlypolicies and activities, contain constituents having a lower predictedtracking error against a benchmark data set, and/or contain constituentsthat have the highest average carbon reductions over time frames.

At step 608, output module 122 may generate an output data set and/orvalues in accordance with user conditions. In some embodiments, outputmodule 122 may output a set of weights that are provided quarterly to aseparate index computation entity. The index computation entity may usethe weights to compute daily index performance and provide the computedindex to one or more financial portals.

In some embodiments, individual public security level risk andoptimization scores (weightings and related data) produced by theclimate module 130, EF converter 124, optimization module 120, andoutput module 122 may be provided to third parties such as dataproviders for inclusion into their own system.

In some embodiments, output module 122 transmit information to clientdevice 102, such that client device may generate or render a graphicaluser interface (GUI) that is configured to display data and informationrelated to the climate module 130, EF converter 124, optimization module120, and output module 122. In some embodiments, the information may beaccessible to a user via application 110 executing on client device 102.

FIGS. 11A and 11B are screen shots illustrating example GUI 1100 and GUI1101, respectively, according to one exemplary embodiment. GUI 1100 mayinclude graph 1102. GUI 1101 may include graph 1104. Each graph 1102,1104 may represent projections for a respective company. As illustrated,each graph 1102 and 1104 visually depicts projections for each companyunder a plurality of scenarios.

FIG. 12 is a screen shot illustrating example GUI 1200, according to oneexemplary embodiment. As illustrated, a user may login to a website toview a dashboard containing data and information related to climate dataanalytics module 104 from client device 102 via application 110. In someembodiments, the dashboard may display time series information forclimate and financial data. The time series information for climate andfinancial data may be generated by climate data integrator 116 andfinancial data integrator 118. The dashboard may also display financialindex information.

Systems and methods of the present disclosure may include and/or may beimplemented by one or more specialized computers or other suitablecomponents including specialized hardware and/or software components.For purposes of this disclosure, a specialized computer may be aprogrammable machine capable of performing arithmetic and/or logicaloperations and specially programmed to perform the functions describedherein. In some embodiments, computers may comprise processors,memories, data storage devices, and/or other commonly known or novelcomponents. These components may be connected physically or throughnetwork or wireless links. Computers may also comprise software whichmay direct the operations of the aforementioned components. Computersmay be referred to with terms that are commonly used by those ofordinary skill in the relevant arts, such as servers, personal computers(PCs), mobile devices, and other terms. It will be understood by thoseof ordinary skill that those terms used herein are interchangeable, andany special purpose computer capable of performing the describedfunctions may be used.

Computers may be linked to one another via one or more networks. Anetwork may be any plurality of completely or partially interconnectedcomputers wherein some or all of the computers are able to communicatewith one another. It will be understood by those of ordinary skill thatconnections between computers may be wired in some cases (e.g., viawired TCP connection or other wired connection) and/or may be wireless(e.g., via a WiFi network connection). Any connection through which atleast two computers may exchange data can be the basis of a network.Furthermore, separate networks may be able to be interconnected suchthat one or more computers within one network may communicate with oneor more computers in another network. In such a case, the plurality ofseparate networks may optionally be considered to be a single network.

The term “computer” shall refer to any electronic device or devices,including those having capabilities to be utilized in connection with anelectronic exchange system, such as any device capable of receiving,transmitting, processing and/or using data and information. The computermay comprise a server, a processor, a microprocessor, a personalcomputer, such as a laptop, palm PC, desktop or workstation, a networkserver, a mainframe, an electronic wired or wireless device, such as forexample, a telephone, a cellular telephone, a personal digitalassistant, a smartphone, an interactive television, such as for example,a television adapted to be connected to the Internet or an electronicdevice adapted for use with a television, an electronic pager or anyother computing and/or communication device.

The term “network” shall refer to any type of network or networks,including those capable of being utilized in connection with environment100 described herein, such as, for example, any public and/or privatenetworks, including, for instance, the Internet, an intranet, or anextranet, any wired or wireless networks or combinations thereof.

The term “computer-readable storage medium” should be taken to include asingle medium or multiple media that store one or more sets ofinstructions. The term “computer-readable storage medium” shall also betaken to include any medium that is capable of storing or encoding a setof instructions for execution by the machine and that causes the machineto perform any one or more of the methodologies of the presentdisclosure.

FIG. 7 is a block diagram illustrating an exemplary computingenvironment 700, according to some embodiments. Computing environment700 includes computing system 702 and computing system 752. Computingsystem 702 may be representative of client device 102. Computing system752 may be representative of organization computing system 104.

Computing system 702 may include processor 704, memory 706, storage 708,and network interface 710. In some embodiments, computing system 702 maybe coupled to one or more input/output (I/O) device(s) 722 (e.g.,keyboard, mouse, display, etc.).

Processor 704 may retrieve and execute program code 716 (i.e.,programming instructions) stored in memory 706, as well as store andretrieve application data. Processor 704 may be included to berepresentative of a single processor, multiple processors, a singleprocessor having multiple processing cores, and the like. Networkinterface 710 may be any type of network communications allowingcomputing system 702 to communicate externally via computing network705. For example, network interface 710 may be configured to enableexternal communication with computing system 752.

Storage 708 may be, for example, a disk storage device. Although shownas a single unit, storage 708 may be a combination of fixed and/orremovable storage devices, such as fixed disk drives, removable memorycards, optical storage, network attached storage (NAS), storage areanetwork (SAN), and the like.

Memory 706 may include application 712, operating system 714, andprogram code 716. Program code 716 may be accessed by processor 704 forprocessing (i.e., executing program instructions). Program code 716 mayinclude, for example, executable instructions for communicating withcomputing system 752 to display one or more pages of website 768.Application 712 may enable a user of computing system 702 to access afunctionality of computing system 752. For example, application 712 mayaccess content managed by computing system 752, such as website 768. Thecontent that is displayed to a user of computing system 702 may betransmitted from computing system 752 to computing system 702, andsubsequently processed by application 712 for display through agraphical user interface (GUI) of computing system 702.

Computing system 752 may include processor 754, memory 756, storage 758,and network interface 760. In some embodiments, computing system 752 maybe coupled to one or more I/O device(s) 762.

Processor 754 may retrieve and execute program code 766 (i.e.,programming instructions) stored in memory 756, as well as store andretrieve application data. Processor 754 is included to berepresentative of a single processor, multiple processors, a singleprocessor having multiple processing cores, and the like. Networkinterface 760 may be any type of network communications enablingcomputing system 752 to communicate externally via computing network705. For example, network interface 760 allows computing system 752 tocommunicate with computer system 702.

Storage 758 may be, for example, a disk storage device. Although shownas a single unit, storage 758 may be a combination of fixed and/orremovable storage devices, such as fixed disk drives, removable memorycards, optical storage, network attached storage (NAS), storage areanetwork (SAN), and the like.

Memory 756 may include climate data analytics module 762, operatingsystem 764, program code 766, and website 768. Program code 766 may beaccessed by processor 754 for processing (i.e., executing programinstructions). Program code 766 may include, for example, executableinstructions configured to perform steps discussed above in conjunctionwith FIGS. 2-6 . As an example, processor 754 may access program code766 to perform operations for assessing climate change risk at asecurity level. Website 768 may be accessed by computing system 702. Forexample, website 768 may include content accessed by computing system702 via a web browser or application.

Climate data analytics module 762 may be configured to predict theenvironmental performance of one or more companies based one or moreenergy sources. For example, climate data analytics module 762 maygenerate one or more environmental metrics for each energy source basedon one or more scenarios selected by an end user. Climate data analyticsmodule 762 may then convert the generated climate data for each energysource into one or more profitability indicators. The climate data tofinancial data conversion allows climate data analytics module 762 todownward correlate each energy source to a respective industry subsectorusing historical price returns of each industry subsector. Within eachindustry subsector, the present system can identify one or morecompanies to which each energy source maps. From this information, thepresent system is able to assess climate change risk associated witheach company based on operations of the company.

Referring next to FIGS. 13 and 14 , example graphical user interfacesillustrating various scenario-predictive/forecast return performanceindicators that may be created by climate data analytics module 114.FIG. 13 is a screen shot illustrating an example graphical userinterface (GUI) 1300, according to one exemplary embodiment. Asillustrated, GUI 1300 may represent a user interface that may begenerated by climate data analytics module 114 to illustrate one or moreoutputs of climate data analytics module 114. For example, an energymixed transition risk-forecast transition risk interaction may bedisplayed via GUI 1300. As illustrated, climate data analytics module114 may integrate one or more outputs generated, for example, using theone or more operations discussed above in conjunction with FIGS. 2-6with traditional stock screening variables that may be retrieved usingfinancial data integrator 118.

Via GUI 1300, an end user may be able to filter various stocks usingtraditional stock characters, such as, but not limited to, market cap,GICS economic sector, and style, along with one or more climate riskscores and rankings generated by climate data analytics module 114.Accordingly, GUI 1300 provides an integrated interface that allows usersto filter stock information based on climate risk scores and ranking, aswell as more traditional financial variables. Conventional systems aresimply unable to produce such an interface due to inaccuracies inreported environmental data, and non-uniformity in the portions ofenvironmental data that are accurate.

Climate data analytics module 114 may transmit the generated GUI 1300to, for example, client device 102, such that client device 102 mayrender GUI 1300 and display GUI 1300 to the user via a display of clientdevice 102. For example, client device 102 may display GUI 1300 viaapplication 110 executing thereon.

FIG. 14 is a screen shot illustrating an example graphical userinterface (GUI) 1400, according to one exemplary embodiment. Asillustrated, GUI 1400 may represent a user interface that may begenerated by climate data analytics module 114 to illustrate one or moreoutputs of climate data analytics module 114. For example, GUI 1400 mayinclude a comparison between actual and forecasted stock performance.

Climate data analytics module 114 may transmit the generated GUI 1400to, for example, client device 102, such that client device 102 mayrender GUI 1400 and display GUI 1400 to the user via a display of clientdevice 102. For example, client device 102 may display GUI 1400 viaapplication 110 executing thereon.

Referring next to FIGS. 15-20D, an example system and method of climatedata processing and impact prediction are described, according toanother embodiment of the present disclosure. Similar to the systemdescribed above, the climate data processing/impact prediction systemdescribed below also may assess climate change risk at a security leveland generate environmental metrics (according to a downward deploymentof profitability indicators, generally referred to below as an energyscore). In addition, the system and method described below may beconfigured to determine a climate transition risk (also referred toherein as a transition risk or T-Risk).

In general, the energy score described above may provide a measure of aresilience of a company and/or indexes of companies to climate change(risk) scenarios. The transition risk (described below) may beconfigured to measure an alignment of a company to a given climatechange scenario. For example, a transition risk metric (e.g., a score)may aid a company in reducing their investment exposure to climatechange and evaluate the relative alignment of a portfolio to certaincarbon goals or targets (e.g., goals established by the United Nationsunder the Paris Accord, the United Nations Framework Convention onClimate Change (UNFCCC), the Intergovernmental Panel on Climate Change(IPCC), etc.).

In order to evaluate and measure a climate change risk, it may bedesirable to consider all potential risks associated with climate,including not only physical risk but also a transition risk. Such ametric may aid in assessing how climate change may impact a company andhow the company is likely to respond to future risk. Assessment ofclimate risk is also becoming an important metric to managing investmentrisk, and may become difficult to quantify if institutions fail toproperly manage their climate risk and transition to a more sustainableoperating structure. Therefore, identifying, measuring, and providing ametric quantify transition risk information may aid in protecting thevalue of a company's current investments.

Both the energy score (described above) and the T-Risk score (describedbelow) of the present disclosure may be used by applying climatescenario analysis to estimate the adaptive capacity and climateresilience of publicly traded companies and their ability to maintainand grow profitability. In general, systems of the present disclosuremay be configured to translate different climate scenarios andsector/regional-specific supply and demand elasticities and marketdynamics such as production volumes, fuel costs, and capitalexpenditures (CAPEX) spending into drivers of financial performancetailored to each industry.

In some examples, the T-Risk metrics may be used for evaluating (withoutbeing limited to) equity securities (e.g., cash equities), fixed incomesecurities (e.g., corporate bonds, convertible bonds) and/or fixedincome derivatives (e.g., call and/or put options). For example, T-Riskmetrics may be used with fixed income securities (together with climatescenario analysis) to estimate the capability of a company to maintainand grow its revenue and profitability indicators. The T-Risk metricsmay allow a user to meaningfully adjust (for example) yields, creditspreads of fixed income issuances as well as the default likelihood ofissuers and envision the impact of climate transition risk (along withtraditional factors) on the user's investments.

In some examples, transition risk may represent a directional climaterisk matrix providing information to improve financial and environmentperformance of a portfolio of securities (such as equity investmentportfolios, fixed income investment portfolios, etc.). The transitionrisk may represent a matrix in that T-Risk metrics may indicate bothclimate sensitivity and a direction of the climate sensitivity. In someexamples, the system may use climate scenario simulation to estimate thetransition risk as a percentage decrease (or possibly increase) inprofitability indicators between Business-As-Usual (BAU) scenario and atarget transition risk scenario (e.g., for a particular company). Insome examples, the transition risk for a company may be recalculated(e.g., on a periodic basis, based on one or more predetermined eventsand the like). In some examples, the transition risk may be normalizedby one or more predetermined dimensions such as market cap, region,sector, style and the like.

In some examples, the transition risk may be configured to meet thedefinition of transition risk laid out by the Task Force onClimate-related Financial Disclosures (TCFD), in that the transitionrisk may indicate any reduction (or increase) in profitabilityassociated with movement from a BAU scenario to a material andrelatively rapid movement to a low carbon environment. In some examples,a confidence interval may be estimated for each transition riskestimate.

The energy scores (metrics) described above may be configured todetermine a climate resiliency. For example, with better exposure toclimate change risk, a user may be in a better position to cope withand/or handle physical and transition climate change risk. The energyscores may consider a minimum and maximum climate scenario and determinea variability of projected share price returns across scenarios. Theenergy scores are generally focused more on the magnitude rather thanthe direction of change in climate scenarios.

In contrast, transition metrics (described below) are generally focusedmore on climate alignments towards a predetermined scenario (goal), suchas alignments towards 1.5-degree and/or 2.0-degree scenarios (e.g.,scenarios representing temperature targets simulated using the globalclimate models achieved in a predetermined future date (e.g., 2100)).The transition risk metrics (scores) may consider both direction andmagnitude. By having information on both magnitude and direction, theT-risk metrics may be used to evaluate which users may be ready forclimate mitigation in terms of profitability and balance sheetindicators and how ready they may be relative to their peers in the samesectors and regions. In some examples, the transition risk mayindication which investments and/or companies may better adapt to1.5-degree and/or 2.0-degree climate scenarios. In general, climateresiliency (as provided by the energy scores described above) may beused to ensure adaption of firms with climate risk changes (e.g., goodor bad, green or brown). Climate alignments (as provided by thetransition risk described below) may evaluate a readiness of a usertowards carbon mitigation and one or more predetermined goals(scenarios), such as the Paris climate goals of the Paris Agreement.

In some examples, the transition risk approach of the present disclosuremay be focused more on climate mitigation rather than adaptation. Thecurrent conventional practices are “inside out” approaches, where dataof different plans, businesses and supply chains is aggregated to gaugehow companies are impacting the environment. In contrast, the T-Riskapproach described herein is an “outside-in” approach. The “outside-in”approach uses science-based algorithms to detect climate alignments atcompany levels. This is computed by translating the information fromclimate models for low carbon scenarios in terms of future returnprojections in low carbon scenarios. In an “inside out” approach, anevaluation may looking into companies from their supply chain, businessmodel, scope 1, 2 and 3 emissions, and the like, to collect data at anoperation level and/or plant level and aggregate that data to form ametric. The “outside in” approach uses macro indicators such as (withoutbeing limited to) policy shocks, economic change, energy transitions,and evaluates how companies may adjust to these changes based on theirhistorical relationship with energy sources.

There are several problems with the conventional “inside out” approach.These problems include biased data (e.g., self-reported data,analysts-based data and the like), limited data (e.g., a lack of regularreporting, gaps in the reporting and the like) and lack of data (e.g.,because reporting may be voluntary) causing data to be unavailable forsome sectors and/or regions. In addition, the “inside out” approachrelies on assumptions on at least a portion of data rather than actualmeasurements (such as assumptions on historical behavior of a company,direction of changes, assumptions with its artificial intelligence (AI)approach, other assumptions). In contrast, the “outside in” approach,when applied at a portfolio and/or index level, show results thatconfirm climate outperformance. Moreover, the “outside in” approach doesnot depend on reporting and data aggregation, and can be very broadlyapplicable to multiple sectors and/or regions.

The transition risk system of the present disclosure solves severaltechnical problems with technical solutions. The technical problemsinclude lack of a suitable approach to convert climate models to climatealignment assessment for a broad range of asset classes that is sectorand/or regionally inclusive, biases in the underlying data and/orinappropriate data that may produce inaccurate metrics, and a lack ofbacktesting capabilities. It is understood that an inability to identifyand remove biases and/or inappropriate data is a technical problem thatrequires a technical solution. Any such errors in the underlying datamay cause the climate risk prediction algorithm (including any climatemodels) to be inappropriately trained and generate inaccurate transitionrisk predictions. It is also understood that an inability of a climaterisk predication algorithm to handle a broad range of data (e.g.,multiple data classes such as asset classes) from a higher level in ahierarchy (e.g., at a sector level and/or a regional level versus at asecurity level) according to a unified approach is a technical problem.For example, the lack of a unified approach for a range of dataclasses/hierarchy may cause the processor of the system to repeatedlyupdate (further develop) the algorithm for new data classes, may causethe algorithm to fail for new data that does not fit a predetermined(e.g., narrow) criterion (and cause the processor to halt the operationcompletely), may cause the memory of the system to store multiple (e.g.,piecewise) algorithms depending on the data class and cause theprocessor to perform additional processing operations to identify theappropriate (piecewise) algorithm and perform the particular processingoperations. Accordingly, the lack of a unified approach may cause anadditional burden on the processor and memory components of the system.Yet further, the lack of backtesting capabilities is also a technicalproblem, because an inability to test and modify the performance of thealgorithm may cause the algorithm to generate inaccurate results.

The technical solutions provided by the present disclosure includetranslating climate models in terms of company level climate alignmentassessment, developing scientific, AI-based algorithms to ensure widecoverage of the transition risk across multiple asset classes (i.e., aunified approach), minimizing biases from self-reported company leveldata, identifying data representative of actual actions rather thanpotential commitments (thereby minimizing inappropriate data),minimizing greenwashing biases, developing techniques that are sectorand regional inclusive and the developing of a backtesting capability.

To translate climate models in terms of company level climate alignmentassessment (e.g., to public equities), the system of the presentdisclosure may use a standardized (unified) procedure across industrygroups and regions, for example, based on mapping company level stockand performance to energy factors for the realized BAU scenario. Insteadof a simple linear regression, the transition risk algorithm may use aHierarchical Linear Model (HLM), which model can avoid single stockinsignificant models, to construct the mapping. This mapping is notbased on self-reported company level information. Rather, the mapping isfocused on historical price dependency of company share price (adjusted)and energy factors carefully captured over a predetermined number oftime periods (e.g., between 40 quarters and 60 quarters). Once themapping is established, future stock returns may be projected for apredetermined time period (e.g., a two year forecast) of each companyfor multiple climate scenarios (e.g., carbon BAU, carbon Paris Alignedand the like).

The transition risk algorithm of the present disclosure includes ascientific AI-based algorithm to ensure wide coverage across assetclass. For example, the algorithm may use common energy factors toensure wide coverage. An Integrated Assessment Model (IAM) may simulatefuture price, supplier cost and demand for a number of energy sources(for example, nine energy sources including oil, gas, coal, biofuels,renewable fuel, renewable electric, hydroelectric, new technology andnuclear). In some examples, in order to explain most of the variancesfor a larger number (e.g., greater than 30) energy factors and reducecollinearity, the transition risk algorithm of the present disclosuremay perform a principal components analysis (PCA) with varimax rotationand select a predetermined number (e.g., 5) of the most significantenergy factors.

The transition risk algorithm of the present disclosure may alsominimize any biases from self-reported company level data. In oneexample, to minimize biases the transition risk algorithm may provide astandardization of the computation processes and may use scientificstatistical models that are rigorously back-tested.

The transition risk algorithm of the present disclosure may also usedata that is based more on action rather than commitments (e.g., wherethe action data may be more relevant than commitments). For example, thealgorithm may rank companies based on their price returns and otherbalance sheet indicators for a responsiveness to energy sources (e.g.,both traditional and non-traditional energy sources). The algorithm mayalso determine forward looking projections of these indicators for bothlow carbon and BAU scenarios. Instead of looking into a company'scommitments, announcements and reporting, the transition risk algorithmmay determine how a company responds to energy markets currently and howthe company will likely respond to multiple climate and energy scenariosin the future. This technique makes the transition risk approach morepractical and action-based, as compared to other theoretical approaches.The transition risk algorithm of the present disclosure may alsominimize greenwashing biases based on the data collected by thealgorithm (e.g., action-based data). In general, greenwashing refers todisinformation disseminated by a company so as to present anenvironmentally responsible public image, such as large commitmentstowards sustainability but no real measurable action.

The transition risk algorithm of the present disclosure may also applyan approach that matures from divestment approaches to more sector andregional inclusive approaches. One example of a divestment approachincludes leaving entire sectors such as energy and utility out of theinvesting portfolios. The divestment approach may be lesscomputationally intensive, but is an incomplete and inaccurate approachfor solving the problem of financing climate transitions.

The transition risk algorithm of the present disclosure may also providebacktesting capabilities: The backtesting capabilities may be used toevaluate data applications both in terms of financial returns andenvironmental outperformance.

To summarize, the transition risk algorithm of the present disclosure(described below) T-Risk may be concerned with climate mitigation. Thealgorithm may analyze a readiness of one or more companies to movetowards predetermined goals (scenarios), such as NetZero goals or ParisAccord goals. The algorithm may consider transitions of companies from acurrent BAU scenario to a Paris Target (for example) scenario. Thetransition risk algorithm provides the ability to integrate the top-downclimate modeling and scenario analysis (described above) together withbottom-up carbon emission data. The T-Risk score may provide a ratingindication of companies based on where they are today (with respect to aBAU scenario) and how ready they are to drive towards NetZero goals (forexample).

FIG. 15 is a functional block diagram illustrating computing environment1500 for climate data processing and impact predication. Computingenvironment 1500 is similar to computing environment 100 (FIG. 1 ), inthat computing environment 1500 may include least one client device 102,financial data source(s) 106 and environmental data source(s) 108.Computing environment 1500 may also include organization computingsystem 1502. Organization computing system 1502 is similar toorganization computing system 104 (FIG. 1 ) in that organizationcomputing system 1502 may include web client application server 112 andclimate data analytics module 114. Organization computing system 1502 isdifferent from organization computing system 104 (FIG. 1 ), in thatorganization computing system 1502 may include transition risk (T-Risk)module 1502. Although not shown, components of computing environment1500 may communication via at least one network (such as network 105shown in FIG. 1 ).

T-risk module 1504 may include energy data integrator 1506, financialdata integrator 1508, energy factor analyzer 1510, T-Risk generator1512, one or more hierarchical linear models (HLMs) 1514, storge 1516,output module 1518, optional portfolio generator 1520 and optionalbacktesting module 1522. Each of energy data integrator 1506, financialdata integrator 1508, energy factor analyzer 1510, T-Risk generator1512, one or more hierarchical linear models (HLMs) 1514, output module1518, optional portfolio generator 1520 and optional backtesting module1522 may be comprised of one or more software modules. The one or moresoftware modules may be collections of code or instructions stored on amedia (e.g., memory of organization computing system 1504) thatrepresent a series of machine instructions (e.g., program code) thatimplements one or more algorithmic steps. Such machine instructions maybe the actual computer code a processor of organization computing system1504 interprets to implement the instructions or, alternatively, may bea higher level of coding of the instructions that is interpreted toobtain the actual computer code. The one or more software modules mayalso include one or more hardware components. One or more aspects of anexample algorithm may be performed by the hardware components (e.g.,circuitry) itself, rather as a result of an instructions.

Energy data integrator 1506 may be configured to receive energy-relateddata from among environmental data source(s) 108. In a non-limitingexample, the energy-related data may include simulated future price,supplier cost and demand for a number of energy sources (e.g., oil, gas,coal, biofuels, renewable fuel, renewable electric, hydroelectric, newtechnology and nuclear). In general, the type of energy-related data mayinclude any suitable type of energy data, and the number of energysources may include one or more energy sources that, together, may beuseful for determining a climate transition risk. Energy data integrator1506 may be further configured to convert the received energy-relateddata into one or more energy returns (see eq. 7 below). The energyreturn data may be used (after further processing described below) forbuilding HLMs 1514, as part of determining the transition risk. In someexamples, the energy return data may include periodic returns that maybe used (after further processing) to generate a training data set forHLMs 1514. Energy return(s) (rather than direct energy-related data) maybe used so that this data is comparable with the dependent variables(stock return data). Thus, energy data integrator 1506 not only obtainsenergy (input) data, but also translates the input data so that it iscompatible with stock return data (obtained by financial data integrator1508). The energy return data determined by energy data integrator 1506may be sent to energy factor analyzer 1510.

Financial data integrator 1508 (similar to financial data integrator 118of FIG. 1 ) may be configured to retrieve financial data from amongfinancial data source(s) 106. In some examples, the financial data mayinclude historical stock return data associated with one or morefinancial securities. In some examples, the stock return data mayinclude quarterly return data for one or more securities. In someexamples, financial data integrator 1508 may use one or more factors forobtaining the financial data from among financial data source(s).Non-limiting factors that may be used include one or more of a price ofextracted oil, a supplier cost to generate electricity using coal, asupplier cost to generate electricity using renewable energy, a demandfor oil and/or any equivalent, a demand for gas and/or any equivalent,and the like. The financial data obtained by financial data integrator1508 may be sent to T-Risk generator 1512.

Energy factor analyzer 1510 may be configured to identify one or moreenergy factors among the energy return data (received from energy dataintegrator 1506), based on principal component analysis (PCA). Theidentified energy factor(s) may then be sent, by energy factor analyzer1510, to T-Risk generator 1512 and used by T-Risk generator 1512 totrain HLMs 1514. In general, energy factor analyzer 1510 may beconfigured to identify energy factor(s) from the energy return databased on a correlation of an energy factor with principal componentsamong the energy return data. To further reduce the number of factorsdetermined via a factor analysis process, a factor with highest absolutecorrelation may be selected for each principal component. In someexamples, the factor analysis process may include PCA with varimaxrotation, to identify suitable energy factors to use for training HLMs1514. The varimax rotation may be used, for example, to explainvariances for a number of identified energy factors (e.g., greater thanabout 30), and may reduce collinearity. In some examples, energy factoranalyzer 1510 may perform PCA with varimax rotation and select apredetermined number (e.g., 5) of energy factors among the energyfactors (e.g., greater than 30) determined to be of a highestsignificance.

In a non-limiting example, the factor analysis process may include PCAwith varimax rotation. Principal Component Analysis may be defined as anorthogonal linear transformation that transforms the energy return datato a new coordinate system such that the greatest variance by somescalar projection of the data comes to lie on the first coordinate(called the first principal component), the second greatest variance onthe second coordinate, and so on. PCA guarantees that a largest varianceis maintained with the fewest independent factors. Varimax rotation maybe performed after the PCA so that principal components can beidentified with actual energy factors having an explanatory power.

T-Risk generator 1512 may be configured to receive energy factor(s) fromenergy factor analyzer 1510 and historical stock return data fromfinancial data integrator 1508. Based on the energy factor(s) andhistorical stock return data, T-Risk generator 1512 may train HLMs 1514,may predict future stock returns (via the trained HLMs 1514), and maygenerate one or more T-Risk scores for the predicted future returnsbased on multiple climate scenarios (e.g., using one or more parametersstored in storage 1516, such as Carbon BAU, Carbon Paris Aligned). Insome examples, the T-Risk score(s) may be adjusted via one or morecarbon emission parameters (stored for example in storage 1516). In anon-limiting example, T-Risk score(s) may also be scaled, such that aunit of output may indicate the number of universe interquartile rangesfrom a universe median. T-Risk generator 1512 is described further belowwith respect to FIG. 16 .

HLMs 1514 may be configured to predict future stock returns forsecurity(s) that take into account energy factor(s). HLMs 1514 may firstbe trained on historical stock return data (via financial dataintegrator 1508) with respect to the identified energy factor(s)(identified via energy factor analyzer 1510). The trained HLMs 1514 maythen be used to predict future stock returns (in accordance with theenergy factor(s)). use more training data which can reduce the varianceof the coefficients' estimates.

The use of an HLM, rather than linear regression techniques, may provideadvantages in cases of sparse data. For example, linear regressiontechniques may be unable to effectively handle cases where a singlestock return may not have a significant model. In general, an HLM is acomplex form of ordinary least squares (OLS) regression that may be usedto analyze variance in outcome variables when the predictor variablesare at varying hierarchical levels. Simple linear regression techniquesmay be insufficient for processing hierarchical data due to theirneglect of the shared variance. In general, an HLM accounts for theshared variance in hierarchically structured data (e.g., lowest level 1such as student level data, higher level 2 such as classroom data, etc.)to accurately estimate lower level slopes (e.g., lowest level 1) andtheir implementation in estimating higher-level outcomes (e.g., higherlevel 2, etc.). An HLM may simultaneously investigate relationshipswithin and between hierarchical levels of grouped data, thereby makingan HLM more efficient at accounting for variance among variables atdifferent levels than other existing techniques. In some examples, HLMs1514 may be configured as a 3-level HLM, where level 1 represents astock (i) at time (t), level 2 represents stock (i) and level 3represents industry group (j) (where i and j represent integers greaterthan or equal to 1 and where i may or may not be equal to j).

In general, HLM models 1514 of the present disclosure may include (a)one or more regression coefficients for various stocks (morespecifically β coefficients, discussed further below) that represent acohort common fixed effect for a stock and (b) one or more regressioncoefficients that represent a random effect of a stock on an industrygroup. For example, FIG. 21 illustrates an example graph of regressioncoefficients of a hierarchical linear model for two petroleum entities(e.g., Exxon Mobil and HollyFrontier) with respect to a supplier cost ofgas and a price of oil, according to an exemplary embodiment. In thisexample, the combination of two petroleum entities represent an exampleindustry group. In this example, both entities may include common cohort2102 having a fixed effect for a stock. Each of the two entities mayhave their own separate random effect 2104-1, 2104-2 (e.g., due to theirrespective supplier cost for gas, price of oil and/or any combinationthereof). In this example, the regression coefficients of the HLM modelmay include coefficient(s) based on cohort common fixed effect 2102 andseparate coefficients based on random effects 2104-1 and 2104-2.

More specifically, a linear regression model is shown in eq. 1

Y _(tij)=α_(ij)+β_(ij) *X _(t)+ε_(tij)  (eq. 1)

where: Y_(tij) represents a dependent variable measured for an i-thstock within a j-th industry group at time t; X_(t) represents a valueon the level-1 predictor; α_(ij) represents an intercept for the stockj; β_(ij) represents a regression coefficient for stock j; ε_(tij)represents a random error associated with the i-th stock within j-thindustry group at time t; E(ε_(tij))=0; var(ε_(tij))=δ²; and varrepresents a variance.

In level-2 models, the level-1 regression coefficients (αij, βij) may beused as outcome variables and may be related to level-2 predictors. Inthe following example, the case of an intercept only model is utilizedwith the below equations:

α_(ij)=γ_(0j) +r _(oij)  (eq. 2)

βij=γ _(1j) +r _(1ij)  (eq. 3)

where: α_(ij) represents an intercept for an i-th stock in a j-thindustry group; β_(ij) represents a slope for an i-th stock in a j-thindustry group; γ_(oj) represents an overall intercept for an j-thindustry group; γ_(1j) represents an overall coefficient for a j-thindustry group; r_(0ij) represents a random effect of an i-th stock in aj-th industry group on the intercept; r_(1ij) represents a random effectof an i-th stock in a j-th industry group on the slope; E(r_(0ij))=0;E(r_(1ij))=0; var(r_(0ij))=τ₀₀; var(r_(1ij))=τ₁₁; cov(r_(0ij),r_(1ij))=τ₀₁; and cov represents a covariance.

Eqs. 2 and 3 may be applied to eq. 1 to form a hierarchical linearmodel, shown in eq. 3 below:

Y _(tij)=γ_(oj) +r _(0ij)+(γ_(1j) +r _(1ij))*X_(t)+ε_(tij)=γ_(oj)+γ_(1j) *X _(t) +r _(0ij) +r _(1ij) *X_(t)+ε_(tij)  (eq. 4)

In eq. 4 the terms γ_(oj)+γ_(1j) represent a the fixed effect and theterms r_(0ij)+r_(1ij) represent the random effect. In eq. 4, it isassumed that:

α_(ij) ˜N(γ_(0j),τ₀₀)  (eq. 5)

β_(ij) ˜N(γ_(1j),τ₁₁)  (eq. 6)

Eq. 1 represents a (regular) linear regression for each stock, which maybe solved using a maximum likelihood estimator, and where α and β arehandled as constants. Eq. 4 represents a hierarchy linear regression. Insome examples, eq. 4 may be solved using a Bayesian estimator, where αand β may be considered to be random and where the prior distribution ofα, β may be estimated using eqs. 3 and 4. In general, estimates obtainedfrom a linear regression in the stock level (eq. 1) may be differentfrom estimates obtained from a hierarchical linear model (eq. 4).Estimates obtained from the hierarchical linear model (eq. 4) may becloser to group means.

Storage 1516 may be configured to store one or more parametersassociated with one or more climate scenarios, such as, without beinglimited to, a Carbon BAU scenario, a 1.5-degree scenario, a 2.0-degreescenario and the like. In general the climate scenario parameters may beassociated with any desired climate scenario, associated with one ormore predetermined goals (such as a Carbon reduction goal) certaincarbon goals or targets (e.g., goals established by UNFCCC), IPCC, andthe like). Storage 1516 may also be configured to store one or morecarbon emission parameters. In some examples, storage 1516 may also beconfigured to store one or more parameters associated with one or moreof energy data integrator 1506, financial data integrator 1508, energyfactor analyzer, T-Risk generator 1512, HLMs 1514, output module 1518,optional portfolio generator 1520 and optional backtesting module 1522.

Output module 1518 may be configured to provide transition risk scoringbased on the results of T-Risk generator 1512. In some examples, theT-Risk score(s) may be part of one or more metrics associated with thetransition risk, as well as (in some examples), additional metricassociated with the energy metrics discussed above with respect to FIG.1 , as well as any other suitable environmental and/or financialmetrics. As discussed above, the T-Risk score(s) may provide aquantitative and objective means for measuring an alignment to a givenclimate change scenario, such as a relative alignment of a portfolio tocertain carbon goals. The T-Risk score(s) may provide information toindicate both a climate sensitivity and a direction of the climatesensitivity for predicted future returns of security(s) with respect toa climate change scenario (such as a transition to a reduced carbonscenario). In some examples, the T-Risk metric(s) provided by outputmodule 1518 may be used (e.g., via client device 102) to adjust variousenergy and/or financial characteristics and review an impact of suchadjustments on the T-risk metrics(s), to improve a financial andenvironment performance of a portfolio of securities.

In some embodiments, output module 1518 (similar to output module 122 ofFIG. 1 ) may produce a website, accessible by one or more users viaapplication 110 (FIG. 1 ) executing on client device 102. The websitemay provide a dashboard that allows users to view results generated byT-Risk generator 1512, and/or optional portfolio generator 1520. In someembodiments, output module 1518 may generate one or more data files forelectronic transfer to client device 102.

Optional portfolio generator 1520 may be configured to use the resultsfrom T-Risk generator 1512 to project a portfolio performance relativeto multiple climate scenarios. Portfolio generator 1520 may optimize theportfolio based on the selected climate scenarios, and the transitionrisk scores and/or metric(s) associated with the selected scenarios. Insome examples, a user, via output module 1518 may be configured tointeract with portfolio generator 1520, such as to adjust energy and/orfinancial characteristics. Portfolio generator 1520 may update theprojected portfolio based on the adjusted characteristics and transitionrisk metric(s).

Optional backtesting module 1522 may be configured to perform one ormore backtesting operations to test a performance of HLMs 1514 andT-Risk generator 1512, and to adjust (if needed) parameters of HLMs 1514and/or T-Risk generator 1512 responsive to the backtesting. In someexamples, HLMs 1514 may be trained using historical data with differenttimes of departure, and one or more historical T-Risk scores may bedetermined (i.e., T-risk score(s) may be determined from historicalreturn data rather than future predicted return data). The historicalT-Risk score(s) may be used to construct one or more climate-friendlyportfolios. The historical financial performance and carbon performanceof the climate-friendly portfolio(s) may be compared with one or morebenchmark values to determine whether the portfolio(s) outperformance orunderperforms the benchmark value(s). Responsive to the benchmarkcomparison, one or more parameters of HLMs 1514 and/or T-Risk generator1512 may be updated, in order to improve the financial and/or carbonperformance of the portfolio. In some examples, backtesting module 1522may perform backtesting periodically and/or in response to one or morepredetermined events (e.g., a new climate goal, a predetermined changein the historical financial data, responsive to user input, and thelike).

FIG. 16 is a functional block diagram illustrating example T-Riskgenerator 1504. T-Risk generator 1504 may include model training module1602, return prediction module 1604, climate scenario adjustment module1606, T-Risk score module 1608 and, in some examples, optional carbonemission adjustment module 1610. Each of model training module 1602,return prediction module 1604, climate scenario adjustment module 1606,T-Risk score module 1608 and optional carbon emission adjustment module1610 may be comprised of one or more software modules. The one or moresoftware modules may be collections of code or instructions stored on amedia (e.g., memory of organization computing system 1504) thatrepresent a series of machine instructions (e.g., program code) thatimplements one or more algorithmic steps. Such machine instructions maybe the actual computer code a processor of organization computing system1504 interprets to implement the instructions or, alternatively, may bea higher level of coding of the instructions that is interpreted toobtain the actual computer code. The one or more software modules mayalso include one or more hardware components. One or more aspects of anexample algorithm may be performed by the hardware components (e.g.,circuitry) itself, rather as a result of an instructions.

Model training module 1602 may be configured to receive one or moreenergy factor(s) 1612 (e.g., from energy factor analyzer 1510) andhistorical stock return data 1614 (e.g., from financial data integrator1508) associated one or more securities, and may be configured to trainHLMs 1514. In some examples, model training module 1602 may beconfigured to perform one or more groupings of historical stock returndata 1614 into one or more hierarchies, such as by sector and/or byindustry, prior to applying historical stock return data 1614 to HLMs1514. For example, an industry group may be used as cohorts to groupstock return data 1614 for training of HLMs 1514. In some examples, whenthere are not enough historical stock returns inside a specific region(e.g., sparse data), model training module 1602 may be configured to goup a level, such as by grouping using sector as a cohort instead of byindustry group. Model training module 602 may be configured to trainHLMs 1514 by regressing (grouped) historical stock return data 1614 onenergy factor(s) 1612 for at least one predefined past period (forexample, see eq. 8).

Return prediction module 1604 may be configured to predict (e.g.,project) one or more future stock returns for one or more future periodsvia HLMs 1514 (as trained by model training module 1602). In someexamples, future stock returns may be predicted and accumulated for apredetermined number of future quarters (e.g., for the next 8 quarters)or any suitable future period of time. In some examples, returnprediction module 1604 may predict future stock returns based on one ormore climate scenario parameters 1616 (e.g., stored in storage 1516). Insome examples, climate scenario parameters 1616 may include one or morepredictions of one or more energy factors under different scenarios.

Climate scenario adjustment module 1606 may be configured to receive thefuture stock returns (from return prediction module 1604 and may adjustthe future stock returns (over the future period(s)) for at least two(or more) climate scenarios. In some examples, the adjustment of thefuture stock returns for climate scenarios may be determined via one ormore corresponding climate scenario parameters 1616 (e.g., stored instorage 1516). For example, climate scenario adjustment module 1606 mayadjust the future stock returns based on a Carbon BAU scenario (e.g., acurrent carbon scenario) to form first adjusted future returns, and mayalso adjust the future stock returns based on a carbon-reduced scenario(e.g., a Carbon Paris Aligned scenario such as a 1.5-degree scenario, a2.0-degree scenario) to form second adjusted future returns.

T-risk score module 1608 may be configured to determine one or moreT-Risk scores 1618 based on the adjusted future stock returns formultiple climate scenarios (received from climate scenario adjustmentmodule 1606. More specifically, T-risk score module 1608 may determineT-Risk score(s) for the future period(s) based on a spread between thefirst adjusted returns (e.g., for a current carbon scenario) and thesecond adjusted returns (e.g., a carbon-reduced scenario), as shown ineq. 10 below. In some examples, multiple T-risk scores may bedetermined, for example, based on a spread between a current carbonscenario and two or more carbon-reduced scenarios (e.g., a 1.5-degreescenario and a 2.0-degree scenario). In some examples, T-Risk score(s)1618 may be scaled by one or more predetermined values. In anon-limiting example, T-Risk score(s) 1618 may be scaled such that aunit of output indicates a number of universe interquartile ranges froma universe median. In another non-limiting example, the scaling mayinclude a standard normalization scaling. In some examples, no scalingmay be applied to T-Risk score(s) 1618 (e.g., so that T-Risk score(s)1618 may indicate an (actual) predicted return reduction from BAU to acarbon-reduced scenario such as a Paris-Aligned scenario).

Optional carbon emission adjustment module 1610 may be configured toadjust the T-Risk score (determined by T-Risk score module 1608 by apredetermined carbon emission adjustment (see eq. 11). In some examples,the carbon emission adjustment may improve both the predicted financialand carbon emission performance for the future stock returns. In someexamples, T-Risk score(s) 1618 may include the carbon emission (asadjusted by carbon emission adjustment module 1610).

Those skilled in the art will appreciate that organization computingsystem 1502 may be configured with more or less modules to conduct themethods described herein with reference to FIGS. 17 and 18 .

As illustrated in FIGS. 17 and 18 , the methods shown may be performedby processing logic that may comprise hardware (e.g., circuitry,dedicated logic, programmable logic, microcode, etc.), software (such asinstructions run on a processing device), or a combination thereof. Inone embodiment, the methods shown in FIGS. 17 and 18 may be performed byone or more specialized processing components associated with T-Riskmodule 1504 of organization computing system 1502 of FIG. 15 and T-Riskgenerator 1512 shown in FIG. 16 .

FIG. 17 is flow diagram illustrating example method 1700 of determininga climate transition risk as part of climate data processing and climateimpact prediction. At step 1702, energy-related data may be obtained, byenergy data integrator 1506 from among environmental data source(s) 108.At step 1704, the energy related data may be converted, by energy dataintegrator 1506, into energy return data. Eq. 7 below illustrates anexample conversion of energy-related data to energy return data for oilfuel price data that may be extracted from among environmental datasource(s) 108. A similar equation may be used to convert otherenergy-related data to energy return data. In eq. 1, t represents time.

$\begin{matrix}{r_{{extracted\_ fuel}{\_ price}{\_ oil}} = {\frac{{extracted\_ fuel}{\_ price}{{\_ oil}\lbrack {t + 1} \rbrack}}{{extracted\_ fuel}{\_ price}{{\_ oil}\lbrack t\rbrack}} - 1}} & ( {{eq}.7} )\end{matrix}$

At step 1706, one or more energy factors may be identified, by energyfactor analyzer 1510, based on PCA. In some examples, the PCA processmay include varimax rotation, in order to avoid any multicollinearity.In a non-limiting example, an initial set of greater than 30 energyfactors may be determined (e.g., including price, supplier cost, unitprofit, etc.), and the energy factors may be reduced to a predeterminednumber (e.g., 5) determined to be of a highest significance. In oneexamples, 5 major energy factors may be determined which can explainaround 80% of the variances.

At step 1708, historical stock return data may be obtained, by financialdata integrator 1508, from among financial data source(s) 106, for oneor more predefined past periods of time.

At step 1710, HLMs 1514 may be trained, by model training module 1602 ofT-Risk generator 1504, by regressing periodic stock return data on theidentified energy factors (step 1706) for a predefined past period(e.g., an N number of quarters) using HLMs 1514. In some examples, step1710 may include training HLM model(s) 1514 by determining cohort commonfixed effects and any random effects associated for each entity byregressing stock return data on energy factor(s) for the predefined pastperiod. In some examples, N=max(min(n,60),40), where n is the length ofavailable history. An example regression of stock regression of stockreturn data on energy factors (F₁-F₅) is shown in eq. 8 below. In eq. 8,F₁-F₅ represents energy factors associated with an extracted price offuel oil (F₁), a supplier cost of fuel with respect to gas (F₂), amarket price of electricity (F₃), a primary energy equivalent tobioenergy (F₄) and a total energy demand (F₅). In eq. 8, a represents anintercept and β_(i) (where i=1−5) represents a sensitivity of stockreturns to the respective energy factor returns. The variable arepresents a stock return drift which may not be influenced by energyfactors.

r _(stock)α+β₁ *F ₁+β₂ *F ₂+β₃ *F ₃+β₄ *F ₄+β₅ *F ₅  (eq. 8)

At step 1712, future stock returns may be predicted, by returnprediction module 1604, for one or more predefined future period(s) viathe trained HLMs 1514. In some examples, predicted returns may beaccumulate for the next 8 quarters.

The T-Risk algorithm, described herein is different from the energyscore described above in that the T-Risk score does not rely onprofitability indicators. An HLM was tested based profitabilityindicators, however improved efficacy results were obtained whenenergy-related data was used for training (rather than profitabilityindicators).

As part of the model training, a beta estimation (vâr ({circumflex over(β)}_(j)) shown in eq. 9) is performed, and may provide an indication ofefficacy of the model (as trained). In general, the beta estimations maybe more stable with increasing training data points. Moreover, aninsignificant beta estimation with large variance may make predictionsuntrustworthy. Ins some examples, In fact, 60 quarters of past periodsshow greater efficacy than 40 past quarters. In eq. 9, the term s²represents a variance of error, the variable n represents a number oftraining data, the term vâr(X_(j)) represents a variance of the jthindependent variable and the remaining term

$\frac{1}{1 - R_{j}^{2}}$

represents a variance inflation.

$\begin{matrix}{{v\hat{a}{r( {\hat{\beta}}_{j} )}} = {\frac{s^{2}}{( {n - 1} )v\hat{a}{r( X_{j} )}}*\frac{1}{1 - R_{j}^{2}}}} & ( {{eq}.9} )\end{matrix}$

At step 1714, the predicted future returns (for the predefined futureperiod(s)) may be adjusted, by climate scenario adjustment module 1606,for a first climate scenario, such as a BAU scenario (e.g., 4.2° C.scenario). At step 1716, the predicted future returns (for thepredefined future period(s)) may be adjusted, by climate scenarioadjustment module 1606, for a second climate scenario, such as a reducedcarbon scenario (e.g., a Ratchet (e.g., 1.32° C.) scenario).

At 1718, at least one T-Risk score may be determined, by T-Risk scoremodule 1608, based on a spread between the adjusted future returns forthe first and second climate scenarios (determined at steps 1714 and1716). An example determination of a T-Risk score based on BAU andRatchet scenarios is shown in eq. 10.

T-Risk=r _(bau) [n]−r _(1.32) [n],  (eq. 10)

where r_bau is the cumulative return under BAU scenario, r_1.32 is thecumulative return under Ratchet scenario and n is the last predictedquarter. In current case, it's the 8th quarter from now.

At optional step 1720, the T-Risk score (determined at step 1718) may beadjusted, by optional carbon emission adjustment module 1610, based on apredetermined carbon emission adjustment. An adjustment for carbonemission is shown in eq. 11.

T-Risk-Carbon-Adjusted=½*T-Risk-Scaled+½*Carbon-Footprint-Scaled  (eq.11)

At optional step 1722, a portfolio of securities may be constructed, byoptional portfolio generator 1520, based on the T-Risk score (includingin some examples, any adjustments to the T-Risk score based on a carbonemission adjustment in optional step 1720). In some examples, aportfolio may be constructed by selecting stocks with better T-Riskscores (e.g., a top 25%).

In some examples, a proposed portfolio may be constructed, and theproposed portfolio may be subject to one or more predetermined efficacyconditions. For example, a financial performance of the proposedportfolio may be compared to a benchmark (or a parent security), toconfirm that the proposed portfolio outperforms the benchmark (or parentsecurity). In addition, a carbon performance of the proposed portfoliomay be determined, to confirm that the proposed portfolio has a reducedcarbon footprint compared to the benchmark (or parent security). In someexamples, the proposed portfolio may also be analyzed for sector weightallocation, such that the allocation is within a predetermined thresholdof the benchmark allocation (or parent security allocation). In someexamples, the proposed portfolio may be output upon meeting thepredetermined efficacy condition(s).

At step 1724, the T-Risk score may be output, for example, by outputmodule 1518, to client device 102. At optional step 1726, theconstructed portfolio may be output, for example, by output, to clientdevice 102. At optional step 1726, optional backtesting module 1522 mayperform backtesting of HLMs 1514 and, in some examples, may adjustparameter(s) of HLMs 1514 and/or T-Risk generator 1512 based on thebacktesting.

Referring to FIGS. 19A-20D, examples of a performance of T-Riskpredictions are shown. In particular, FIGS. 19A and 19B are examplegraphs of cumulative returns before and after carbon emission isadjusted; and FIGS. 20A-2D are example graphs of cumulative returns as afunction of date for various rebalancing operations (with respect torebalancing quarterly, every year, every 2 years and every 3 years,respectively).

In some examples, carbon adjustments may not only improve a financialperformance, but may also improve the carbon emission performance. Asshown in FIG. 19A, the use of a T-Risk score alone shows already showsfinancial and carbon outperformances (with respect to the benchmark). Asshown in FIG. 19B, adding a carbon emission adjustment to the T-Riskscore may improve both financial and carbon outperformance (with respectto the benchmark). In some examples, adding a T-risk score to carbonemission may make large a financial outperformance more stable. Ingeneral, as shown in FIGS. 19A-20D, a portfolio constructed using theT-risk score may include large financial outperformance compared with abenchmark or its parent security (e.g., an exchange traded fund (ETF)).Moreover, adding carbon emission information to the T-Risk score mayhelp to significantly reduce the carbon footprint for a constructedportfolio.

FIG. 18 is a flow diagram illustrating example method 1800 of generatinguser-customizable transition risk prediction information via aninteractive graphical user interface (GUI), such as the GUIs describedabove. At step 1802, organization computing system 1502 may receive arequest for transition risk analysis from an interactive GUI generated(or rendered) by client device 102. At step 1804, the GUI may prompt theuser to select a climate scenario (e.g., a reduced carbon scenario). Atoptional step 1806, the GUI may prompt the user to input one or moreother desired characteristics (such as desired financialcharacteristics, desired environmental characteristics, desired entitycharacteristics, desired security characteristics and the like).

At step 1808, the selected scenario (and any other desiredcharacteristics) may be input to a simulation tool represented by T-Riskmodule 1504. At step 1810, one or more T-risk scores (e.g., a T-riskscore without carbon emission adjustment, a T-risk score with carbonadjustment, multiple T-risk scores for different reduced carbonscenarios, etc.) may be generated by T-Risk module 1504, based on theselected climate scenario (e.g., according to method 1700 shown in FIG.17 ). At optional step 1812, a portfolio may be constructed, by optionalportfolio generator 1520, based on the T-Risk score(s) and (in someexamples) other desired characteristics (obtained at step 1808).

At step 1814, one or more of the T-Risk score(s), portfolio metrics (ifa portfolio is constructed in optional step 1812) and (in some examples)other suitable metrics (for example, metrics related to the T-riskscore(s), metrics related to an energy score, other information onfinancial and/or environmental characteristics and the like) may bepresented to client device 102, by output module 1518, via the GUI.

At step 1816, organization computing system 1502 may monitor the GUI todetermine whether additional user input is received. At step 1818, it isdetermined whether additional user input is received. When additionaluser input is received, step 1818 proceeds to step 1820, and steps1810-1820 may be repeated. When no additional user input is received,step 1818 proceeds to step 1816, and organization computing system 1502may continue to monitor for additional user input. In this manner,system 1502 may adjust the results presented at step 1814 depending onany user input updates.

The technique for generating T-risk scores (by T-risk module 1504 oforganization computing system 1502) is different from the energy scoresdetermined by system 104 of FIG. 1 . One difference is with respect toclimate scenario. The T-Risk technique measures a return spread from BAUscenario to a lower carbon scenario (such as a Paris Aligned scenario(˜1.32 degree C.)). In addition provides an relative value metric asopposed to an absolute value metric. This is because the T-Risk scorerepresents a difference between BAU and reduced carbon scenarios withoutrescaling by the BAU scenario.

In addition, the energy score represents a scalar whereas the T-Riskscore represent a vector. This is because the T-Risk score is adirectional climate matrix which indicates not only a climatesensitivity but also the sensitivity direction. Another difference isthat the T-Risk score uses energy-related data (directly) whereas theenergy score uses profitability indicators as part of their respectiveprocessing techniques.

In some examples, the T-Risk technique may use a more detailedclassification to group stocks compared with the energy score technique.One further difference from the energy scores is that the T-Risktechnique uses a hierarchical linear model with industry and regiongrouping vs Linear Model.

While the foregoing is directed to embodiments described herein, otherand further embodiments may be devised without departing from the basicscope thereof. For example, aspects of the present disclosure may beimplemented in hardware or software or a combination of hardware andsoftware. One embodiment described herein may be implemented as aprogram product for use with a computer system. The program(s) of theprogram product define functions of the embodiments (including themethods described herein) and can be contained on a variety ofcomputer-readable storage media. Illustrative computer-readable storagemedia include, but are not limited to: (i) non-writable storage media(e.g., read-only memory (ROM) devices within a computer, such as CD-ROMdisks readably by a CD-ROM drive, flash memory, ROM chips, or any typeof solid-state non-volatile memory) on which information is permanentlystored; and (ii) writable storage media (e.g., floppy disks within adiskette drive or hard-disk drive or any type of solid staterandom-access memory) on which alterable information is stored. Suchcomputer-readable storage media, when carrying computer-readableinstructions that direct the functions of the disclosed embodiments, areembodiments of the present disclosure.

While the present disclosure has been discussed in terms of certainembodiments, it should be appreciated that the present disclosure is notso limited. The embodiments are explained herein by way of example, andthere are numerous modifications, variations and other embodiments thatmay be employed that would still be within the scope of the presentdisclosure.

1. A system, comprising: a processor; and a memory having programminginstructions stored thereon, which, when executed by the processor,causes the processor to perform an operation, comprising: receiving, viaat least one network, a user selection of a climate change scenario froma remote client device; retrieving, over the at least one network,energy data from among one or more energy sources, the energy datacomprising one or more of simulated future price data, supplier costdata and demand data for the one or more energy sources; retrieving,over the at least one network, historical financial data directed to oneor more securities from one or more remote financial data sources;predicting one or more future returns for the one or more securities,through a simulation of a climate change impact of the one or moreenergy sources, by applying the historical financial data and the energydata to at least one hierarchical linear model (HLM), the at least oneHLM configured to predict how the one or more remote financial datasources respond to multiple future climate scenarios based on measurableaction data among the historical financial data, the measurable actiondata indicative of a responsiveness of the one or more remote financialdata sources to current energy market data, thereby minimizing one ormore self-reporting biases among the one or more remote financial datasources; generating a climate transition risk for the one or moresecurities based on the predicted one or more future returns adjusted inaccordance with the selected climate scenario, the climate transitionrisk is a metric based on a correlation between the one or moresecurities and climate sensitivity and indicates an amplitude anddirection of the climate sensitivity on a given portfolio of investmentsincluding the one or more securities, the climate sensitivity indicatingfinancial effects caused by a transition to the selected climatescenario on the given portfolio of investments including the one or moresecurities; providing a data set representing the climate transitionrisk to the remote client device, the data set including one or moremetrics for evaluation of the climate sensitivity of the given portfolioof investments; responsive to the data set provided to the remote clientdevice, receiving, by the processor, additional user input indicative ofan adjustment of the climate sensitivity associated with the evaluationof the one or more metrics; and adjusting, by the processor, the climatesensitivity by adjusting the one or more metrics provided to the remoteclient device, in accordance with the additional user input received. 2.The system of claim 1, wherein the processor is further configured toperform an operation comprising: converting the energy data to energyreturn data, such that the energy return data is compatible with the oneor more securities; and identifying a predetermined number of energyfactors from the energy return data, wherein the processor is configuredto predict the one or more future returns by applying the historicalfinancial data and the predetermined number of energy factors to the atleast one HLM.
 3. The system of claim 2, wherein the predeterminednumber of energy factors are identified from the energy return databased on a principal component analysis (PCA) of the energy data, basedon a correlation of the predetermined number of energy factors withprincipal components among the energy data.
 4. The system of claim 2,wherein the processor is further configured to perform an operationcomprising: training the at least one HLM by regressing the historicalfinancial data on the predetermined number of energy factors for atleast one predefined past period.
 5. The system of claim 1, wherein theprocessor is further configured to perform an operation comprising:adjusting the predicted one or more future returns based on a firstclimate scenario and the selected climate scenario, to form respectivefirst adjusted returns and second adjusted returns; and generating theclimate transition risk for the one or more securities based on a spreadbetween the first adjusted returns and the second adjusted returns. 6.The system of claim 5, wherein the first climate scenario includes abusiness as usual (BAU) scenario and the selected climate scenarioincludes a predetermined reduced carbon scenario.
 7. The system of claim1, wherein the processor is further configured to perform an operationcomprising: adjusting the climate transition risk based on apredetermined carbon emission.
 8. The system of claim 1, wherein theprocessor is further configured to perform an operation comprising:constructing a portfolio based on the climate transition risk.
 9. Thesystem of claim 1, wherein the processor is further configured toperform an operation comprising: backtesting the at least one HLM. 10.The system of claim 1, wherein the one or more securities include atleast one of equities, fixed income and fixed income derivatives.
 11. Acomputer-implemented method, the method comprising: receiving, by acomputing system comprising a processor and a memory, via at least onenetwork, a user selection of a climate change scenario from a remoteclient device; retrieving, over the at least one network, by thecomputing system, energy data from among one or more energy sources, theenergy data comprising one or more of simulated future price data,supplier cost data and demand data for the one or more energy sources;retrieving, by the computing system, over the at least one network,historical financial data directed to one or more securities from one ormore remote financial data sources; predicting, by the computing system,one or more future returns for the one or more securities, through asimulation of a climate change impact of the one or more energy sources,by applying the historical financial data and the energy data to atleast one hierarchical linear model (HLM), the at least one HLMconfigured to predict how the one or more remote financial data sourcesrespond to multiple future climate scenarios based on measurable actiondata among the historical financial data, the measurable action dataindicative of a responsiveness of the one or more remote financial datasources to current energy market data, thereby minimizing one or moreself-reporting biases among the one or more remote financial datasources; generating, by the computing system, a climate transition riskfor the one or more securities based on the predicted one or more futurereturns adjusted in accordance with the selected climate scenario, theclimate transition risk is a metric based on a correlation between theone or more securities and climate sensitivity and indicates anamplitude and direction of the climate sensitivity on a given portfolioof investments including the one or more securities, the climatesensitivity indicating financial effects caused by a transition to theselected climate scenario on the given portfolio of investmentsincluding the one or more securities; providing a data set representingthe climate transition risk to the remote client device, the data setincluding one or more metrics for evaluation of the climate sensitivityof the given portfolio of investments; responsive to the data setprovided to the remote client device, receiving, by the processor,additional user input indicative of an adjustment of the climatesensitivity associated with the evaluation of the one or more metrics;and adjusting, by the processor, the climate sensitivity by adjustingthe one or more metrics provided to the remote client device, inaccordance with the additional user input received.
 12. Thecomputer-implemented method of claim 11, the method further comprising:converting, by the computing system, the energy data to energy returndata, such that the energy return data is compatible with the one ormore securities; and identifying, by the computing system, apredetermined number of energy factors from the energy return data,wherein the predicting, by the computing system, further comprisespredicting the one or more future returns by applying the historicalfinancial data and the predetermined number of energy factors to the atleast one HLM.
 13. The computer-implemented method of claim 12, whereinthe predetermined number of energy factors are identified from theenergy return data based on a principal component analysis (PCA) of theenergy data, based on a correlation of the predetermined number ofenergy factors with principal components among the energy data.
 14. Thecomputer-implemented method of claim 12, the method further comprising:training, by the computing system, the at least one HLM by regressingthe historical financial data on the predetermined number of energyfactors for at least one predefined past period.
 15. Thecomputer-implemented method of claim 11, the method further comprising:adjusting the predicted one or more future returns based on a firstclimate scenario and the selected climate scenario, to form respectivefirst adjusted returns and second adjusted returns; and generating theclimate transition risk for the one or more securities based on a spreadbetween the first adjusted returns and the second adjusted returns. 16.The computer-implemented method of claim 15, wherein the first climatescenario includes a business as usual (BAU) scenario and the selectedclimate scenario includes a predetermined reduced carbon scenario. 17.The computer-implemented method of claim 11, the method furthercomprising: adjusting, by the computing system, the climate transitionrisk based on a predetermined carbon emission.
 18. Thecomputer-implemented method of claim 11, the method further comprising:constructing, by the computing system, a portfolio based on the climatetransition risk.
 19. The computer-implemented method of claim 11, themethod further comprising: backtesting, by the computing system, the atleast one HLM.
 20. The computer-implemented method of claim 11, whereinthe one or more securities include at least one of equities, fixedincome and fixed income derivatives.
 21. A non-transitory computerreadable medium having instructions stored thereon, which, when executedby a processor, cause the processor to perform an operation comprising:receiving, via at least one network, a user selection of a climatechange scenario from a remote client device; retrieving, over the atleast one network, energy data from among one or more energy sources,the energy data comprising one or more of simulated future price data,supplier cost data and demand data for the one or more energy sources;retrieving, over the at least one network, historical financial datadirected to one or more securities from one or more remote financialdata sources; predicting one or more future returns for the one or moresecurities, through a simulation of a climate change impact of the oneor more energy sources, by applying the historical financial data andthe energy data to at least one hierarchical linear model (HLM), the atleast one HLM configured to predict how the one or more remote financialdata sources respond to multiple future climate scenarios based onmeasurable action data among the historical financial data, themeasurable action data indicative of a responsiveness of the one or moreremote financial data sources to current energy market data, therebyminimizing one or more self-reporting biases among the one or moreremote financial data sources; generating a climate transition risk forthe one or more securities based on the predicted one or more futurereturns adjusted in accordance with the selected climate scenario, theclimate transition risk is a metric based on a correlation between theone or more securities and climate sensitivity and indicates anamplitude and direction of the climate sensitivity on a given portfolioof investments including the one or more securities, the climatesensitivity indicating financial effects caused by a transition to theselected climate scenario on the given portfolio of investmentsincluding the one or more securities; providing a data set representingthe climate transition risk to the remote client device, the data setincluding one or more metrics for evaluation of the climate sensitivityof the given portfolio of investments; responsive to the data setprovided to the remote client device, receiving, by the processor,additional user input indicative of an adjustment of the climatesensitivity associated with the evaluation of the one or more metrics;and adjusting, by the processor, the climate sensitivity by adjustingthe one or more metrics provided to the remote client device, inaccordance with the additional user input received.
 22. Thenon-transitory computer readable medium of claim 21, wherein theoperation further comprises: converting the energy data to energy returndata, such that the energy return data is compatible with the one ormore securities; and identifying a predetermined number of energyfactors from the energy return data, wherein the predicting furthercomprises predicting the one or more future returns by applying thehistorical financial data and the predetermined number of energy factorsto the at least one HLM.
 23. The non-transitory computer readable mediumof claim 22, wherein the predetermined number of energy factors areidentified from the energy return data based on a principal componentanalysis (PCA) of the energy data, based on a correlation of thepredetermined number of energy factors with principal components amongthe energy data.
 24. The non-transitory computer readable medium ofclaim 22, wherein the operation further comprises: training the at leastone HLM by regressing the historical financial data on the predeterminednumber of energy factors for at least one predefined past period. 25.The non-transitory computer readable medium of claim 21, wherein theoperation further comprises: adjusting the predicted one or more futurereturns based on a first climate scenario and the selected climatescenario, to form respective first adjusted returns and second adjustedreturns; and generating the climate transition risk for the one or moresecurities based on a spread between the first adjusted returns and thesecond adjusted returns.
 26. The non-transitory computer readable mediumof claim 25, wherein the first climate scenario includes a business asusual (BAU) scenario and the selected climate scenario includes apredetermined reduced carbon scenario.
 27. The non-transitory computerreadable medium of claim 21, wherein the operation further comprises:adjusting the climate transition risk based on a predetermined carbonemission.
 28. The non-transitory computer readable medium of claim 21,wherein the operation further comprises: constructing a portfolio basedon the climate transition risk.
 29. The non-transitory computer readablemedium of claim 21, wherein the operation further comprises: backtestingthe at least one HLM.
 30. The non-transitory computer readable medium ofclaim 21, wherein the one or more securities include at least one ofequities, fixed income and fixed income derivatives.